Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control

Large-capacity wind farms may increase the risk of torsional vibration of turbine generator shafts in the wind-thermal-bundled power transmitted by series-compensated system, therefore the analysis and suppression strategy of sub-synchronous oscillation (SSO) in such system needs to be thoroughly studied. In this paper, the mechanism of electromechanical torsional interaction and the influence of wind farm integration on system oscillation are analyzed. In order to suppress the SSO, the mechanism of electromagnetic torque increment produced by additional damping control of static synchronous compensator (STATCOM) is studies, based on which, a SSO damping controller is designed. Based on the complex torque coefficient approach and time domain simulation approach simulation cases are built in DIgSILENT/PowerFactory. The results show the impact of wind farm integration and series compensation degree on system damping and verify the suppression effect of STATCOM on system SSO.

Wind damage is one of the common disaster that has serious influence on China’s power grid. The wind damage has a wide distribution and a low success rate of reclose, which makes the transmission line outage more likely. In this study, it is found that the 2016 wind damage of power grid in China’s major areas mainly distributed in the southeastern coast, southwest region, north China and its surrounding areas. In temporal distributions, wind damage mainly occurs in summer and afternoon. In 2016, the first reclose success rate of wind damage trip is only about 10% during 330 kV and above voltage level transmission lines. 220 kV is a voltage level threshold of trip line: tripps number increases with voltage level decreases when above the threshold, otherwise trips number decreases with voltage level decreases. Transmission line wind damage trip trend has a large interannual variation from 2010 to 2016. This study analyzes the characteristics of wind damage trips in 2016, which provides guidance for wind damage prevention work in windbreak-prone areas and windbreak-prone periods.

The wind power and load are both affected by the meteorological factors. Studying on the correlation between wind power and load in different weather conditions is beneficial to reduce the double uncertainties on the sides of source and load, and significant to the planning, dispatching, safe and stable operation of the electric power system. First, the improved algorithm of traditional K-means clustering algorithm—X-means is adopted to divide the daily meteorological factors of wind speed and temperature, which mainly affect the wind power and load. Then, the Pearson, Kendall and Spearman correlation coefficients are used to analyze the correlation between wind power and load variables in different weather conditions. Finally, the optimal Copula function is selected from four commonly-used Copula functions to describe the joint distribution of wind power and load in each weather condition. Furthermore, the data in another place is used to verify the correlation between wind power and load with the weather condition. The conclusions are as follows: (1) the X-means algorithm can realize the effective classification of weather conditions. (2) The positive correlation between wind power and load is mainly concentrated in summer or near summer, the negative correlation between them is mainly concentrated in winter or near winter. (3) The correlation between wind power and load is quite varying in different weather conditions, but the joint distributions are basically consistent with the Archimedes Copula functions, and the tail correlation coefficients of their distribution are zero under most weather conditions. (4) There is a repeated rule between wind power and load with the weather condition.

To solve the problem of operation optimization for regional integrated energy system, a kind of operation optimization method considering operating cost and reliability is proposed. Firstly, the difference between multi-objective integrated energy system and traditional dispatching is analyzed. A multi-objective operation optimization framework of integrated energy system is introduced. Secondly, the output characteristics of the main integrated energy system equipment are analyzed. Thirdly, the objective function with the lower operating cost and the higher reliability is established. Then, taking into account the system constraints of electricity, heating and cooling, the multi-objective running optimization model is solved by linear programming method. Finally, an integrated energy system scene with distributed generation and energy storage equipment is constructed. The Pareto solution set of the scene is searched. Meanwhile, the different operating modes of CCHP are compared. The results show that the proposed model can meet the requirements and provide a unified optimization solution for integrated energy system.

A long-term scale voltage optimization control strategy for distributed photovoltaic actively participated with distribution network considering active reduction is proposed. The residual capacity of PV inverters outside the active power is used to participate in the reactive power optimization for voltage control. If the voltage still exceeds the threshold values, the active power curtailment strategy to PVs is applied to provide more capacity for reactive power optimization and maintain the voltage in range. Considering the uncertainties of photovoltaics and load outputs, the complex affine method is employed to establish the power output model in each time period. Combination of the Ybus power flow calculation and the linear decreasing weight particle swarm optimization is used to solve the proposed optimization model. Case studies on an IEEE 33-bus system are conducted to verify the effectiveness of the proposed optimization strategy.

To cater for the commercial application of energy storage on the user side, a two-stage optimal configuration model of energy storage on the user side based on generalized Benders Decomposition algorithm is proposed. Firstly, according to the collected historical load data, the user can judge whether it is suitable to install the energy storage device. Then, aiming at maximizing the benefit of energy storage, a two-stage optimal energy storage allocation model with monthly scale and daily scale is established for users who are suitable for installing energy storage devices. Considering that the model is a nonlinear mixed integer optimization, the generalized Benders Decomposition algorithm is used to optimize the model. Finally, the load data of three typical large industrial users are collected, and the Benders Decomposition algorithm is compiled with MATLAB2017a to optimize the simulation, which verifies the economic effectiveness of the proposed model.

Aiming at the problem that the transmission efficiency and transmission power are difficult to achieve at the same time in the coupled resonant radio energy transmission process, based on the modeling and analysis of the equivalent structure of wireless power transmission S-S structure, this paper gives a method to synchronize transmission power and transmission efficiency under different loads and different distances, and further obtains that the optimal working frequency and the matching load resistance at a certain distance can make the power-efficiency synchronization reach the maximum value. By means of MATLAB simulation software, the experiment platform is set up to analyze the variation rule of system transmission efficiency and transmission power under different loads and different distances. The results demonstrate the correctness of the previous theory and provide a certain reference for improving the transmission efficiency and power of radio energy.

Considering that air source heat pumps have the advantages of fast heat supply response, high heat supply efficiency and environmental protection, air source heat pumps can quickly follow the change of wind power when wind power changes rapidly, and timely eliminate wind power. Air source heat pumps are considered in the day-ahead dispatch model of integrated electricity-heating system in this paper. The model regards generation cost for traditional units and combined heating power (CHP) units, operation cost for air source heat pumps and electric boilers and curtailment cost of wind power as its optimization target. The response speed of air source heat pumps and electric boilers are first considered in the constraint conditions. The wind power can be converted into heat energy by air source heat pumps so that improve the consumption of wind power when the wind curtailment is serious. The heat generated is used to provide system heat. The excess of heat can be stored in the thermal storage energy devices, which released to provide system heat at low troughs. Finally, the comparison between the examples shows that air source heat pumps not only can quickly respond to the change of wind power, but also have low operation cost and reduce system power generation cost .

With the rapid development of new energy vehicles in China, the trend of decommissioning of power batteries has arrived, and the stagger utilization of decommissioned power batteries has become the focus of the industry. Once a power battery is decommissioned, the retired power battery needs to undergo relevant tests and evaluations before it can be carried out the cascade development and utilization. In this paper, we creatively propose the AHP-CRITIC method. Firstly, according to the health characteristics of the decommissioned power batteries, the improved K-means cluster analysis is used to analyze the curve characteristics of the decommissioned power batteries. Secondly, for the economic indicators of decommissioned batteries, we take advantage of the AHP-CRITIC method to evaluate the decommissioned power batteries. Then, a comprehensive evaluation model that synthetically considers the health characteristics and economics of decommissioned power batteries is established. Finally, an example is given to verify the effectiveness of the proposed evaluation model.

By virtual synchronous generator (VSG) technology, the inverter can be controlled simulating the operation characteristics of synchronous generators and regarded as an access of renewable energy. The harmonics contained in the power grid brings the same order harmonics to the grid-connected current generated by VSG, result in an increasement of harmonic content in the power grid. In order to solve this problem, an improved VSG control strategy is proposed. Firstly, the equivalent model of VSG is established. Based on the analysis of the impact of harmonics on the grid-connected current generated by VSG, an improved algorithm for obtaining inductance current reference value based on double second-order generalized integral is proposed. Inertial integral link is used to replace differential operation in full feed-forward control for grid voltage, and the mechanism of current regulator is analyzed. The current tracking and harmonic suppression can be realized by choosing the corresponding current regulator. Finally, a simulation and experimental platform is built to verify the validity of the improved VSG control strategy.

The expansion of business management and outward service of new market participants, for example integrated energy service corporations, results in the increasing demand for developing the integrated energy management and service system. By analyzing the business feature of integrated energy, a comprehensive system architecture, which features distributed self-control, centralized coordination, hierarchical operation and bidirectional interaction, is proposed to match the characteristics of multi-type, multi-operator, multi-customer and multi-service requirement of IES. Core techniques including comprehensive steady energy-flow model, IES comprehensive decision-making based on hierarchical analysis were studied. The effectiveness, applicability and functional completeness of the proposed system architecture are demonstrated by its successful application in practical projects such as Tongli Project.

The continuous integration of new energy sources has aggravated the fluctuation of power load in power systems. In recent years, the rapid development of energy storage technology has matched the demand for the balance of supply and demand of power load. At the same time, the peak and valley electricity price policy of power system makes it possible for the investor to make a profit with the investment of building energy storage systems. So it is necessary to plan the construction of the energy storage system from the perspective of investor. Based on the internal rate of return of investment, considering the various financial details such as annual income, backup electricity income, loan cost, income tax, etc., this paper establishes a net cash flow model for energy storage system investment, and uses particle swarm optimization algorithm based on hybridization and Gaussian mutation to get the energy storage capacity that maximizes the internal rate of return of the investment. And this internal rate of return is compared with the set internal rate of return of the investment to determine whether the energy storage system is worth building. The paper illustrates the effectiveness of the investment planning model through the planning process of two users.

In recent years, with the large number of renewable energy connected, the problems about intermittence and volatility of renewable energy grid-connected have gradually emerged. Micro grid has flexible and controllable characteristics, that’s the foundation for solving the grid-connected problem of distributed generation, also that’s the inevitable choice to solve the problem that the high proportion of renewable energy connected to the grid. Compared with substations, the requirement for synchronized connection of micro grids has changed greatly. This paper introduces coordination control method of synchronized connection for micro grids and application. It fully considers the characteristics of multi-operation modes and load power fluctuation of micro grids, based on GOOSE (Generic Object Oriented Substation Event) transmission mechanism, it realizes real-time adjustment of voltage and mode switching through controlling energy storage devices. it meets the application requirements of micro grid grid-connected, it also realizes smooth connection of micro grid. The control device based on this method has been successfully applied in the demonstration a national 863 project of Luxi Island Micro grid.

In order to solve the problem that the seasonal DC load causing the energy’s idle in other seasons and the inability of the power exchanging from DC to AC side during the abnormal operation of AC/DC Hybrid microgrid (MG), this paper first proposes a mobile energy storage (MES)’s transfer strategy and then establishes a two-layer optimal configuration model of AC/DC hybrid MG considering seasonal DC load and the MES’s transfer strategy, which takes the minimum cost of MG’s life cycle as the outer level objective function and the minimum average daily rental amount of MES as the inner level objective. The results of configuration of WT, ES, inverter and MES are compared, and the charge and discharge power characteristics of ES and MES during normal or abnormal operation are analysed in case study, which verifies that both the MES and MES’s transfer strategy improve the economy of MG considering seasonal DC load.

A method to evaluate the consistency of battery packs was proposed in this article. With such evaluation, the administrator of the energy storage system could understand the deterioration of the battery packs and remove the abnormal state to avoid the potential failures and extend the battery life. First of all, selecting the indicators of the evaluation which can effectively reflect the consistency. And then calculating the fuzzy relation matrix by applying the fuzzy functions to the operating data from the battery management system (BMS); employing the anti-entropy weight method to obtain the weights of each indicator. Finally, acquiring the consistency grade with the weights and relation matrix. The method is applied to a storage system to verify that the evaluation result is aligned with the real-time observation.

Traditional distribution network planning model is established with 0–1 variables, and its efficiency will be greatly reduced in the face of large-scale distribution network planning. Based on the basic ideas of path planning and mesh method, a mesh method for distribution network planning model is proposed in this paper. Firstly, the distribution network planning area is abstracted, divided and merged according to mesh method to form the distribution network planning topology. The upper-level planning model is established with the reliability and economy as the objective function, and the connection relationship of the grid nodes in the planning area is optimized with the elite ant colony algorithm. After connection is determined, the connection switch and spare line are determined based on the shortest path method as the lower level planning model. Considering distribution network connection mode, economy and reliability, a bilevel planning model is established. In order to verify the effectiveness of the model and algorithm proposed in this paper, the model and algorithm are applied to calculate in an unbuilt business district in Guangzhou. The calculation results show that the method proposed in this paper can obtain the planning scheme and various economic and reliability indexes under different connection modes at a fast speed, which provides a reference for planners.

With the rapid increase of wind power centralized grid-connection scale, the security and stability of power grid are greatly affected. In order to simulate the dynamic characteristics of wind farm accurately, a large-scale wind power online equivalent method based on multi-source data fusion is proposed. The operating state of the state estimation modeling network is identified by the fusion of multi-source real-time data such as SCADA, PMU and security and stability control system. When the error of branch power flow or node voltage at the boundary is large, the active power regulation range of wind power generators is determined by comprehensively considering the actual measurement collected by the wind farm centralized control system and the prediction information of the wind power prediction system. The active power of each wind farm is determined by quadratic programming model. The wind power generators are grouped according to the topology in the wind farm, then each group is subdivided according to static characteristic and operating state in turn. The dynamic models and static parameters of equivalent wind power generators and equivalent transformers are calculated. The proposed method is proved to be fast and effective through the analysis of a practical power grid example.

Based on the typical timing characteristics of Distributed Generation (DG) and user power load, considering the uncertainty of large-scale electric vehicles and the environmental benefits of different distributed power sources, the operating cost, network loss and environmental benefit of the distribution network are used as the objective function. In this paper, a Monte Carlo simulation method is used to simulate the charging characteristic of the electric vehicle, and the model is solved by the binary bat algorithm. By comparing with a single-purpose distributed power optimization configuration model, the simulation results verify the rationality and validity of the proposed model and method.

The energy hub with renewable energy integrated with cold, heat, electricity and gas can improve energy efficiency, demand side flexibility and environmental benefits. To deal with the fluctuation of net load and promote the accommodation of renewable energy, an optimal configuration scheme of energy hub considering system flexibility is presented. In this paper, the mathematical model of the energy hub structure considering the integrated demand response is firstly established. Then, the flexibility demand of the energy hub is obtained according to wind power output, photovoltaic output and load characteristic curves of typical days in different seasons. Meanwhile, two sets of flexibility indexes, system upward/downward flexibility supply and upward/downward system flexibility potential, are proposed. Furthermore, an optimal capacity configuration model of energy hub aimed at annual total cost consisting of economic cost, inadequate system flexibility penalty cost and environmental protection cost is established. In this model, the system flexibility adequacy constraints are introduced and integrated demand response is taken into account. Finally, different energy supply modes and demand response strategies under flexibility adequacy constraints are simulated and analyzed. The results show that the integrated demand response can significantly improve the economy of the configuration scenarios, and the proposed model can effectively improve the flexibility of the integrated energy system.

Due to its own volatility and intermittentness, wind power has increased the burden on the power system after the grid connection, which has a serious impact on its safe operation. Considering the parallel and serial control strategy adopted by traditional-wind storage system, and taking into account the economy and the effect of primary frequency modulation, this paper puts forward an optimal control strategy for comprehensive utilization of energy storage system (ESS). Finally, a comprehensive evaluation method for the energy storage system to participate in the primary frequency modulation of the power grid is proposed. And in the current domestic frequency modulation market, the evaluation method is used to verify that the optimal control strategy has the best frequency modulation performance.

The configurations of multi-energy storage devices in the regional integrated energy system (RIES) can greatly improve the economic benefits of the system and it is an important research direction of RIES planning. However, at present the research on the optimization of electric/thermal/gas multi-energy storage configuration in RIES is insufficient. Under this background, the method to deploy the rated capacity and power of multi-energy storage systems for RIES containing power to gas (P2G) device was proposed in this paper. Bi-level optimization model was established in the purpose of considering the interaction between the configuration and operation of the multi-energy storage systems at the same time. The upper level was optimized for configuration, and the lower level was optimized for operation. By the Kuhn–Tucker approach, the bi-level problem was transformed into a single level optimization problem that can be solved with Gurobi optimizer. Finally, the correctness of the proposed model was verified by an example.

In order to meet the increasing reliability requirements of urban distribution network, based on the typical wiring of urban cable ring network, combined with three typical automated protection modes, this paper uses the analytical method to study the data about power outage caused by faults occurred on outlet circuit breakers, line segments, subsection switches, load switches and distribution transformers (DTs) respectively. Then the corresponding reliability improvements are put forward. The analysis results show that to assure the reliability of class A region, the centralized distribution automation and load switches should be adopted instead of decentralized system and subsection switches. To assure the reliability of class A+ region, the scheme of differential protection plus self-healing should be used, with subsection switches and tie switches replaced by circuit breakers and each circuit breaker in the ring main units (RMUs) enclosed in either Vacuum or SF6 box separately.

The disturbance of integrated power will increase while the rotational inertia that can restrain the change of system frequency will decrease when large-scale renewable energy is integrated to the power grid. This produces new problems and challenges for AGC Control. Based on the model of sub operating area, this paper proposed the fundamental principle of AGC control when large-scale renewable energy, thermal and hydro units are participating in the process of frequency regulation. These three types of resources can undertake the total ARR (Area Regulation Requirement) of the whole operating area by equal proportional regulation capacity ratio strategy or priority coordinated control strategy. Besides, this paper also described how to track inner stable tie-lines and how to coordinate with upper level load dispatch center to jointly regulate system frequency. These research results are helpful for practical applications of AGC regulation.

To promote the coordinated development between distributed wind generation and distribution network, and to improve the absorptive capacity of wind generation, a hybrid energy storage system allocated in the distribution network is put forward, which is composed by battery and small-scale compressed air energy storage system. And the optimal configuration method is discussed. The battery is used to mitigate the wind power output fluctuation thanks to its quick response capacity, and the small-scale compressed air energy storage system is used for peak regulation thanks to its flexible allocation and large energy density. The operational principle of the hybrid energy storage system is discussed, and the peak load regulation method of the small-scale compressed air energy storage system is presented. Based on the research of the allocation of hybrid energy storage system, the capacity optimization and the optimal operation, the optimal configuration method of the hybrid energy storage system is discussed. The proposed model and the method are verified on the modified distribution network, and the results prove the effectiveness and flexibility of the model.

Although battery energy storage technology has been born for a long time, it is mainly built with new energy power generation. This paper focuses on the back to back test of battery energy storage stations, which is seldom carried out in the field of new energy. On the basis of structure anatomy and principle analysis, combined with the engineering debugging example of Changsha Langli energy storage station, the back to back test method, process and mechanism of battery energy storage stations based on semi-isolated bidirectional converter are deeply analyzed, and PDP protection action and group control of the back to back test are studied and solved. Functional verification and other key issues are expected to provide useful reference for relevant practitioners and engineering applications.

Wind and photovoltaic(PV) power generation have strong randomness, fluctuation and intermittence, so it is necessary to configure energy storage devices in micro-grid to suppress its output fluctuation. Aiming at the characteristics of islanded operation mode of micro-grid, a coordinated control strategy of hybrid energy storage system with lithium batteries (Li-b) and super capacitors (SC) is designed. The two-stage filter structure of the first-order low-pass filter and the second-order high-pass filter is adopted to distribute the load power gap. On this basis, considering the state of charge (SOC) of Li-b and the voltage of SC, a cooperative control strategy of hybrid energy storage system is designed. Then, according to the coordinated control strategy, the control structure of the two-stage energy storage converter (including DC/DC converter and DC/AC converter) is designed. Finally, a simulation model is built in MATLAB/Simulink to verify the effectiveness of the control strategy of the hybrid energy storage system designed in this paper.

This paper comprehensively considers the economic, environmental and reliability impacts of energy supply systems and establishes a two-level multi-objective optimization configuration model for multi-energy complementary park integrated energy systems with multiple energy supply equipment. The upper layer has the minimum annual cost as the objective function; the lower layer has the lowest annual operating cost, the highest annual energy supply reliability and the best DG output characteristics as the objective function. The improved immune genetic algorithm based on fuzzy membership and variance weighting is used to solve the model nested. The conclusion shows that the correctness and validity of the model and algorithm.

The proximity of B and L indices to 1 indicates the level of voltage instability of power system and load points, which varies with changing system conditions. To quantify the effect of control measures, the sensitivities of B and L indices to the control measures are newly proposed, based on which the adverse control effects with different sites of load shedding, the non-optimality of local load shedding, the opposite effects of tap adjustment, and the inconsistency of L and B indices with the same control measures, are quantified. The numerical results validate the accuracy of the sensitivity model by comparing with the continual calculation.

The continuous growth of electrical load, a high cost of electricity, environmental protection, renewable energy utilization, and power quality problems have become severe challenges facing the power industry. As an important part of the future energy internet, the development of micro-grid has been widely concerned by all sectors of society. Based on specific engineering projects, this paper discusses the feasibility of the application of integrated micro grid system in industrial parks from the perspectives of load characteristics, solutions, control methods and investment returns.

In this paper, a new local quadratic wavelet neural network (LQWNN) method is proposed for short-term wind power generation prediction in power system. Due to the nonlinear structure of its local quadratic model (LQM), this method can effectively simulate the nonlinear behavior of wind power generation. In the time domain and frequency domain, wavelet function sets the effective region of LQMs in LQWNN, thus improving the learning efficiency and structural transparency of the overall model. The proposed LQWNN method employs a simple and effective particle swarm optimization (PSO) algorithm and optimizes its parameters. Through two real cases studies, namely the wind power forecast of the Irish power grid and the Jiuquan wind power plant in Gansu province, the performance of the proposed wind power forecasting method is evaluated and compared with other methods. The results show that the LQWNN method proposed in this paper is very effective in predicting wind power generation and has a good application prospect.

In this paper, a medium and long term output analysis model of multipoint access decentralized wind power based on Markov chain Monte Carlo method is proposed. The model considers the correlation among multi-point access decentralized wind farms and the multivariate first order Markov process is obtained by analogy with univariate higher order Markov process. Based on this, the state transition matrix of multi-point access decentralized wind power output is established, and the output distribution of four decentralized wind farms in Shantou is simulated according to the model. The statistical parameters such as mean, standard deviation, probability density function (PDF) and autocorrelation coefficient (ACF) of the generated wind power time series and historical series are compared. The results show that the statistical parameters of the simulated series are very close to those of the historical series. The model presented in this paper is very effective. Then this paper proposes a method for selecting typical daily output of decentralized wind power based on hierarchical clustering algorithm, and simulates the typical daily output of summer and winter of four decentralized wind farms in Shantou based on the model.

With the digitalization and intellectualization of substations, the amount of state data collected by intelligent components is becoming larger and larger. Traditional data mining technology cannot meet the requirements of real-time data processing and application speed. For a typical smart substation, a fault diagnosis method based on improved association rule mining algorithm is proposed for transformer near-zone fault analysis. Firstly, an improved association rule data mining algorithm based on rough set is designed to extract information from massive data and diagnose faults. Then genetic algorithm is applied to improve association rule data mining algorithm to speed up data processing. Finally, simulation analysis is carried out to demonstrate the effectiveness and rapidity of the proposed method. The results show that the intelligent substation fault diagnosis method based on improved association rule mining algorithm has fast and powerful reduction ability in data processing. It can extract useful data from intelligent substation components to accurately judge fault information, reduce data scale and improve fault processing speed.

This paper introduces the system structure of the kilometer-level superconducting cable demonstration project of Shanghai and presents an overall scheme of relay protection configuration for superconducting lines. The scheme takes differential protection and quench protection as the primary protection for quick isolation of the internal faults, and takes over-current protection as the back-up protection for timely removal of superconducting cables when there are line faults or external faults after the differential protection is out of service, ensuring the safety of the superconducting cable equipment. Also, the feasibility of application of reclosure and inverse time over-current is discussed. Aiming at the wiring method of the kilometer-level superconducting cable demonstration project of Shanghai, this paper introduces the tripping and closing logic and matching mode of breakers on both sides of the superconducting line, so as to realize a consistent supply of electricity when the superconducting cable is in fault. Non-electric quantity information such as temperature, pressure, flow rate, etc. is an important monitoring indicator for the state of the superconducting cable. The application of non-electric quantity status information on both ends of the kilometer-level superconducting cable line in relay protection device is introduced.

Aiming at second equipment lacks comprehensive and effective state detection and simple and reliable evaluation method, a state perception method of intelligent substation secondary system based on fuzzy comprehensive evaluation (FCE) and deep convolutional neural network (DCNN) is proposed. Firstly, combining with the factors of auxiliary equipment state evaluation, the FCE method is adopted to evaluate the influence degree of each secondary equipment. Secondly, DCNN was used to learn the regional and edge features respectively, and the significance and non-significance confidence of the detected region was obtained. Finally, combined with the influence degree of each secondary equipment and the significant and non-significant confidence level, the state of the secondary equipment in intelligent substation is evaluated. The experiment results indicate that the proposed method can effectively solve the deficiency of the corresponding equipment status detection and evaluation method of intelligent substation.

In the risk assessment for the smart substations, the traditional Monte Carlo-based methods rely on prior distribution knowledge and cannot cover all the potential failure scenarios. In this paper, a risk assessment method based on the deep neural network for the smart substation secondary system is proposed. Firstly, a deep neural network established by the deep auto-encoders is proposed to quantitatively evaluate the operational risk of the smart substation. Secondly, the key indicators that affect the substation operations are hierarchically combed and refined, and are used as the inputs of the deep neural network. Finally, numerical simulation results from an actual smart substation show that compared with the traditional Monte Carlo-based assessment methods, the accuracy of the proposed method for assessing the operation states of the smart substation can be improved by 48.03% under the same iterations. In addition, the running time of the proposed method is less by 12.3% than the time of traditional method in the case with the same iterations. Hence, effectiveness and feasibility of the proposed method can be verified.

Aiming at the problem that description of physical link information in SCD file is blank and physical link cannot be quickly located by alarm information of virtual link failure in operation and maintenance of substation, this paper proposes to extract physical link information from SPCD file, extend physical link model in SCD model, and construct mapping between virtual-real link in smart substation. This paper provides technical support for the visualization of physical circuit of substation and a feasible scheme for fast fault location.

According to the requirement of gas chromatography identification in transformer oil of substation, an adaptive threshold chromatographic identification algorithm is designed. Traditional first derivative method only uses slope threshold to recognize chromatographic peaks, so the degree of automation is low and it is easy to be distorted. In view of these shortcomings, this paper improves the first derivative method. Based on the data pre-processing and peak identification algorithm, the appropriate threshold is determined, and the peaks are identified by combining the peak curves and the characteristics of the standard gas. Since the threshold parameter of the algorithm can be fixed, the overlapping peak detection is less affected by human influence, and further improves the accuracy of chromatographic identification.

The hardware platen on relay protective panels is the functional demarcation point and the blind spot of condition monitoring between protection device and the control circuit of primary equipments. Realizing the condition monitoring of the hardware platen is the basic need to promote the application of the ubiquitous internet of things in the relay protection specialty. In this paper, a method based on the Hall sensing effect is proposed to detect the state of the hardware platen in the substation, which can realize the automatic detection of the state of the hardware platen without disturbing the outlet control circuit. This paper first proposed the hall element selection scheme for hardware platen state detection, and then developed the configuration scheme of hardware platen state detection device for two kinds of the hardware platen, separated type and line spring type, designed hardware platen state detection integrated circuit, based on that an experimental platform was constructed and the validity and reliability of the proposed scheme was verified.

This paper studies a new sampling and control on-site module based on mature SOC chip technology, which is installed near primary equipment; and proposes a new protection and control system scheme in smart substation using on-site module and HSR ring network technology. It analyzes the delay characteristics of the application of HSR technology to implement the data transmission scheme of the on-site module, which shows that the scheme can meet the fast tripping requirements of the protection; proposes network flow control solution to improve network reliability, and proposes data transmission delay measurement technology to improve the synchronization reliability of network sampling, which effectively ensures the reliable application of the multi-bay protection that using the sampling and tripping method by network. The new protection and control system scheme simplifies the secondary system architecture, meets the technical development direction of intelligent primary equipment, and is beneficial to ensure the reliability and fast tripping of the protection device.

The traditional fiber optical current transformer uses Y-branch waveguide modulator. However, Y-branch waveguide modulator produces abnormal noise current when it is subjected to vibration stress caused by the double-arm optical structure between the modulator and the coupler. In this paper, a new optical structure using reflective waveguide modulator is proposed because it has ability to eliminate the additional phase difference caused by vibration stress. Both results of laboratory and substation prove that reflective waveguide modulator can not only effectively reduce the impact of environmental stress on fiber optical current transformer, but also can eliminate the abnormal current caused by circuit breaker switching vibration. This study provides a new way of thinking and direction for the design and manufacture of high performance current transformer applying to high voltage and large current measurement field.

In current design and configuration stages of smart substation, manual configuration of virtual terminal connection relation is needed, while the result would be generated repeatedly and user needs to select template for automatic connection based on template, that is likely to make mistake and difficult for check and management. Hence proceeding from the primary and secondary devices association in the SSD file, research on the virtual terminal connection relation template for typical bays of various voltage levels and connection types is made. A method of smart substation virtual circuit automatic connection is proposed. By realizing automatic process of bay mapping, device mapping and virtual terminal mapping from the model in the SCD to the template in system configuration tool of smart substation, this method could improve automation level and accuracy of system configuration and be used to check the correctness of the virtual terminal connection relation in the SCD file.

The traditional communication technology between substations of security and stability control system (SSC) has some inherent shortcomings. It does not conform to the trend of current technological development, and restricts the expansion and application of SSC. According to the IEC 61850-90-1 standard, using intelligent substation process layer GOOSE communication technology, a new communication technology between substations based on IEC61850 in SSC is proposed in this paper. The key technology of communication network architecture, communication capacity control, data flow control and visualization of decoupling configuration are deeply researched, and the typical project of Brazil Belo Monte SSC project is introduced. This technology has several advantages, such as stable and reliable communication, data transmission capacity, reusability and interoperability. It can meet the demand of information exchange between substations, and it represents the development direction of the communication technology of SSC.

With the wide application of IEC 61850, the actual electrical connection of the cable in the intelligent substation has been replaced by the other media such as the optical fiber, which directly causes the invisibility of the secondary circuit and brings inconvenience to the operation and maintenance. To solve these problems, the visualization technology of the secondary circuit of intelligent substation based on CIM/G is proposed in this paper. The automatic parsing technology of the secondary circuit connection relationship in the SCD file is introduced, together with the automatic generation technology of the visual interface. The visualization technology adopted here not only realizes the static layout of the physical circuit and the logic circuit in the monitoring interface, but also achieves the dynamic refresh of the graphical objects which reflects the real-time status of the circuit. Finally, the effectiveness of the proposed methods is successfully demonstrated by the practical application, and the comprehensive performance of this automatic visualization technology is evaluated to be superior to the traditional manual mode.

Wide-frequency synchrophasor is the general term for synchrophasors of harmonics and interharmonics from low frequency to high frequency. Limited by existing transfer standard and transmission channel bandwidth, the current Wide Area Measurement System (WAMS) cannot transfer wide-frequency synchrophasors and can only transfer fundamental synchrophasors. This paper provides a wide-frequency synchrophasor transfer method from substation to master station, and focuses on the channel time division multiplexing (TDM) technology. The compatibility of the transfer method based on this technology with the existing protocol and the network characteristics are analyzed. At last, the feasibility of the method is verified by simulation experiments. Within the channel TDM method, the wide-frequency synchrophasor can be transferred from substation to master station under the existing WAMS architecture and transfer standard, and the transfer method satisfies the requirements of the existing physical channel bandwidth.

At present, the equipment of substation is becoming more and more complex and GIS is the main equipment in substation. In this paper, According to the basic concept of IEC61850 technology system. The interoperability of relay protection devices for different kinds of different manufacturers is summarized. Combined with 3/2 wiring mode and protection configuration of a UHV substation. Analyze UHV substation’s trip accident and expected failure modes. A method of optimizing the protection of 3/2 wiring is proposed to better deal with the uncertain events in substations.

This paper introduces a visual component aggregation method. This method aggregates multiple function blocks to generate new function block or component, and supports aggregation between multiple components. Based on this method, a unified visual development system is implemented. The visual development system supports a device research and development process combining visual programming and configuration technology, provides the visual reuse function for the research and development of relay protection device and improves the efficiency of visual programming and configuration.

The power system is becoming larger and larger, and the operation is more frequent, which puts higher requirements on the real-time performance of analysis and calculation. The graph database is a new type of database that originated from the parallel processing of massive data in the Internet. The data model can visually express the topology of the grid and easily realize parallel traversal query. Firstly, the characteristics of graph database are introduced from the aspects of data model and data query, and the potential advantages of applying it to large-scale power system analysis and calculation are analyzed. Secondly, the design method of power system modeling is presented for satisfying the guidelines of integrity, consistency and efficiency conforming with CIM/E standard. Finally, a parallel power network topology analysis algorithm is implemented based on the graph database model for a provincial grid. The calculation results of the actual large-scale provincial power grid show that the proposed method can significantly improve the topology search efficiency.

Comprehensive assessment of power quality is the basis for measuring power quality. SFSM (Sequential Floating Search Method) with Bhattacharyya distance is used to select the most effective power quality evaluation indices, then use three classical classifiers to verify the validity of the selected indices. Through analyzing the data collected by the monitoring points, each evaluation indicator is evaluated and screened. The reserved evaluation indices are searched by SFSM algorithm with the Bhattacharyya distance. SVM (support vector machine), KNN (K-nearest neighbor) and RBF (radial basis function) neural network are used to test the classification accuracies of the selected evaluation indices. The experimental results show that this method can find the best combination of power quality indices, which can reasonably evaluate the comprehensive evaluation level of power quality for a monitoring point.

The existing machine learning methods for the decision-making of power grid operation lack sufficient interpretability and focus on transient stability assessment. In response to this deficiency, this paper analyzed the process of humankind learning and decision-making in the field of power grid operation. To further speed up the decision making process, a model which combines correlation analysis, deep learning and decision tree used for key constraint variable identification, transient stability assessment and feasible region generation is proposed. This model is able to take advantage of feature extraction and fitting effect of deep learning, good interpretability of correlation analysis and decision tree. Experiment results on a subsystem in China Southern Power Grid demonstrate the feasibility and effectiveness of the model.

The cloud-to-ground lightning is a serious weather disaster, which has great harm to human security and fundamental construction. This paper explores the spatial-temporal distribution of cloud-to-ground lightning at Hubei Province in 2015. We firstly compute the Moran’s index and the local Moran’s index of the cloud-to-ground lightning density in each month. The distribution of the occurrence count, density and Moran’s indexes is a parabola, and the peak locates in April and May of 2015. Then, we test the Moran’s index results by p-value. The analysis shows that about 10% cloud-to-ground lightning region is clustered. Finally, we observed the probability of the lightning density is Quasi-Poisson distribution and fit the lightning density by General Additive Model (GAM). The experiment indicates precipitation, terrain roughness, altitude can be used for effectively predicting the ground lightning density level.

To improve the reliability of power supply, medium and low voltage distribution network always uses small current neural grounding in China. When the single phase to ground fault occurs, the fault feature is weak and fault section is hard to locate by traditional method. The development of distribution level phasor measurement units (D-PMUs) makes the visual and precise synchronous data available. In this paper, a new fault section location method based on Convolutional Neural Network (CNN) is proposed. The data cross-section is the input characteristic matrix of CNN model corresponding to the snapshot of the synchronous measurements of multi D-PMUs at a specific time, including current and voltage phasor. And the fault section and confidence of results would be given by the trained three-layer CNN model. This method only requires the snapshot data of single moment in the steady fault stage, so it is not affected by the fault initial phase angle and is robust to the noise and data loss or corruption. The accuracy, anti-interference capacity, and effectiveness of proposed method had been tested on a 20-node distribution network with distributed generation in PSCAD.

Based on the traditional user portrait technology and concept, this paper extracts various indicators that reflect the characteristics of grid operation state from the main aspects of grid operation to construct the grid state portrait label system and label generation method, establishes the grid state portrait analysis model, and evaluates the grid state trend. On this basis, the grid state portrait application system is designed and implemented, and the in-depth analysis of the grid state portrait and operation data is realized by means of an intuitive comprehensive display method. The practical application results show that the system can intuitively reflect the grid operation situation from multiple dimensions, discover and display data rule related to equipment or grid, which can provide technical support for power grid operators to conduct monitoring, take preventive measures and make decision for power grid operation.

With the interconnection and inter-supply of power systems in various regions, the emergence of multi-voltage class electromagnetic ring networks, the convergence of conventional load flow algorithms is facing severe challenges, and real-time calculation of theoretical line loss cannot be realized. In this paper, the community discovery idea of complex network theory is applied, and the parallel calculation method for line loss of power grid partition based on improved Fast-Newman algorithm is proposed. The method makes full use of the advantages of Fast-Newman algorithm to deal with the large-scale complex networks. On the basis of network Weighted, combined with the characteristics of power grid to improve the algorithm’s agglomeration process, and the rapid partition of the power grid is realized. Further, by formulating the equivalence principle of the partition boundary, the partitions are completely separated. On the basis of partition independence, the SPMD parallel architecture in MATLAB is used to complete the parallel computing of each partition. Finally, the regional power grid is taken as an example to verify that the method has good accuracy, and its calculation speed and convergence are in line with the requirements of real-time calculation.

When the fuzzy C-means clustering algorithm (FCM) is applied to solve the problem of daily load curve clustering analysis, its performance is usually affected by selection of the initial clustering center and the sample similarity is often characterized directly by distance of each samples, which causes clustering easy to fall into local optimum. In this paper, the daily load characteristic value index is used to deal with the data dimension reduction of the daily load curve and a fuzzy C-means clustering algorithm optimized by grey wolf optimizer (GWO-FCM) is proposed. GWO-FCM uses GWO to optimize the initial clustering center for FCM, which combines the global search capability of GWO and the local search capability of FCM The results shows that the proposed method can perform daily load curve clustering analysis effectively and obtain good robustness.

State-of-the-art Graphics Processing Unit (GPU) has superior performances on float-pointing calculation and memory bandwidth, and therefore has great potential in many computationally intensive power system applications, one of which is batch-Electromechanical transient time simulation (ETTS) of power system. The computation speeds of the traditional method is slow. When dealing with batch simulation in multiple scenarios, the power consumption of multi-machine cluster system is large and the acceleration effect is limited by the number of processors. This paper proposes a superior GPU-Accelerated algorithm for batch-ETTS based on the implicit integration alternating solution method (IIASM), which extracts data-level fine-grained parallelism and increases the efficiency of memory access by combed design in solving batch dynamic element injection current and batch sparse linear systems (SLS). By offloading the tremendous computational burden to GPU, the algorithm of this paper can limit the time in one alternate iteration of single ETTS within 0.5 ms for one system with over 10,000 nodes.

Modular design is a planning mode which improves the rationality and exactness of power network design. When analyzing operational reliability of distribution network, current methods are not in real-time and lack accuracy which means operational reliability analysis based on real-time operation data can better meet the needs of future modular planning. This article presents a method of modular power distribution room operational reliability evaluation of based on data learning. Firstly, take advantage of the ELM neural network to deal with distribution network data and establish the learning model of organ outage factor. Secondly, compute the outage rate of organs and organ sets according to the result of the learning model. Build power availability calculation model of load node in module and form operational reliability evaluation index of modular power distribution room. In the end, use examples to test and verify the effectiveness of the method proposed. The result shows that the operational reliability evaluation method based on ELM neural network presented in this article is effective.

The traditional method of conducting regular inspections for high-voltage transmission towers is mainly based on manual inspection. Workers need to board the tower for visual inspection during operation, which is not only unsafe, but also inefficient. With the gradual popularization of drones in the use of power industry, automated inspections based on artificial intelligence and image processing technology have become possible. In the complex background of aerial images, doing defect detection for key equipment components of high-voltage transmission lines is a challenging problem, and achieving the goal of target recognition of transmission line equipment is the basis of defect detection. Based on the deep learning technology, this paper researches the target recognition of transmission line equipment by using aerial image, focusing on insulator and anti-vibration hammer. The process is as follows: Firstly, the image data should be preprocessed. Secondly, the data should be marked and the data set should be divided. Then the two networks of Faster R-CNN and YOLOv3 are used for training. Finally, the trained model is evaluated.

In recent years, with the rapid development of the power market and smart grid, higher and higher requirements are put forward for load forecasting technology, and deep learning is also widely used in the filed. Aiming at the small load base of bus load, strong time series and large influence by relevant factors, this paper proposes a short-term bus load forecasting method based on CNN-GRU neural network. The method processes the input historical load, date type, weather data, renewable energy generation, time-of-use electricity price and other related factors through the CNN network, intelligently extracts the dominant factors and compresses the generated timing feature vectors, and then performs bus load forecasting through the multi-layer GRU network. Taking several bus load data from a certain city in the east from 2012 to 2018 as samples/tests, CNN-GRU and traditional BPNN forecasting methods were used for prediction. The experimental results show that CNN-GRU deep neural network has higher precision and better prediction effect when dealing with bus load forecasting.

Aiming at the new requirement of natural language human-computer interaction in the new generation of power grid dispatch and control system, this paper designs and implements intelligent voice interaction service based on the voice engine of companies such as iFLYTEK and SinoVoice, combining the technology of Microservice, Redis database, Word2Vec, etc. By shielding the interface differences of commercial voice engines and providing a simple and consistent service-based interface, it is conducive to the convenient development of voice-related applications in the power grid dispatch and control system. The platform can flexibly change and expand the underlying commercial voice engine according to the actual needs, manage the context in the multi-round dialogue process. It can also store a large amount of voice information to provide support for later data mining and user behavior analysis.

Power grid dispatching fault disposal documents are essential for dispatching operators while it is a challenge for them to use these documents efficiently and promptly due to unstructured text data. Recently a new language representation model, BERT (Bidirectional Encoder Representations from Transformers) has created state-of-the-art models for a wide variety of natural language processing tasks with fine-tuning. In this work, we propose a semantic knowledge matching approach based on redesigned fine-tuned BERT model for dispatching fault disposal documents. The experimental results demonstrate that our proposed approach achieves significant sentence-level semantic accuracy for the offline knowledge such as previous fault handling records and fault disposal documentations, The semantic knowledge matching result can also be referred for online fault handling automatically and promptly to improve grid disposal handling efficiency. This approach can be deployed as common one for natural language proceeding tasks of power grid.

Using the stroboscopic mapping model, the ZCS resonance working point of the ICPT system is obtained, and the single-channel 2FSK signal modulation and demodulation are realized based on the resonance point switching. In order to eliminate the influence of transmission signals of different resonance points on the load voltage, a buck converter is added and the controller is designed. The simulation and experimental results show that the technology can effectively reduce the volume and cost of the ICPT system while realizing wireless power and information transmission and constant voltage output.

Short-term load forecasting predicts the hourly load of the future in few minutes to one-hour steps in a moving window manner based on historical and real-time data collected. Effective forecasting is the key basis for in-day scheduling and generator unit commitment in modern power system. It is however difficult in view of the noisy data collection process and complex load characteristics. In this paper, a short-term load forecasting method based on empirical mode decomposition and deep neural network is proposed. The empirical modal number determination method based on the extreme point span is used to select the appropriate modal number, so as to successfully decompose the load into different timescales, based on which the deep-neural-network-based forecasting model is established. The accuracy of the proposed method is verified by the testing results in this paper.

Typhoon is among the worst nightmares for the high voltage power transmission network operators in coastal areas. It’s long been a challenge to predict the typhoon caused faults. This paper first analyzed mechanism of typhoon caused high voltage overhead transmission lines faults. Then proposed a numerical weather forecast based typhoon disaster prediction method. The relation between gust and 10-min average wind speed is analyzed and the criteria for determine jumper wire swing and tower structure damage risk level are proposed. Lastly, the experience of the application of proposed prediction method in Zhejiang province is introduced. The proposed method has been proved to be effective in prediction of typhoon disaster, though the numerical weather forecast model should be improved to represent the terrain effect more accurately.

A new high-power laboratory is constructed by SGCC, there are some new equipment adopted in the laboratory. In the paper, we talk about the configuration of the laboratory and illustrate the equipment layout method of such testing system. First, we choose different circuits to meet the aim of the high power tests with some compromises. Second, we calculate most test parameters of synthetic test circuit, the key calculating procedure is transforming the variables of equations to dimensionless form. It will make the number of independent variables reduce from 8 to 5. Third, overvoltage is simulated between different devices by ATP-EMTP program, it is important to take parasitic parameters of the circuit into account. At last, we get a reasonable insulation distance between different devices by some non-standard equipment test and insulation coordination method, it is the theoretical basis for equipment layout design.

In power system, current unbalance is a kind of common fault that seriously affects the safety and efficiency of power system. There are many reasons that may cause three-phase unbalance, and now rely on manpower to judge according to specific conditions. This paper proposes a new algorithm for clustering analysis of unbalanced three-phase current data. We define a serials of feature parameters, and then use self-organizing competitive neural network for clustering analysis to subdivide current unbalance into five categories. In the experiment, a large amount of historical current data is analyzed by the proposed algorithm. We get five categories with obvious features and differences. The clustering results are reasonable and interpretable. The algorithm makes full use of the large amount of unmarked historical data produced by power system, and is helpful for the early warning of current unbalance and pre-judgment of causes.

The aim of this paper is to diagnose the bushing faults by making use of the infrared images. Before diagnosing the bushing faults, the preprocessing and segmentation should be processed. The preprocessing, which includes adaptive median filtering, K-means, opening operation, is applied to filter the noise and remove the background. To segment the bushing from the image, simple linear iterative clustering (SLIC) is applied which allows the bushing segmented according to the shape of it and makes it works better. After analyzing the thermal characteristics of the fault image of bushings from the DL/T-664-2016 Application rules of infrared diagnosis for live electrical equipment, it can be concluded that diagnose the fault area’s relative position can be a great way to diagnose the bushing, so it’s regarded as a principle to classify the faults. In this paper, we selected some typical infrared bushing images with faults. After the processing, the results have been verified that the method is able to segment the bushing and diagnose the fault preliminarily.

Natural disasters (such as snowstorms, typhoons, etc.) can result in a large number of physical failures in distribution network. Optimizing the rush repair strategy of physical failures in post-disaster restoration can reduce economic loss and accelerate the restoration process. In current research, the optimization of repair path and strategy for a single repair team is studied, while the complexity of rush repair and the travel cost are not fully considered, and the effect of tie lines in restoration cannot be ignored. Therefore, an optimization of rush repair strategy method is established in this paper considering the combined action of repair teams, tie lines and the dynamic change of the repair strategy caused by network reconfiguration. With the objective of minimizing the total loss during the restoration, the status of repair teams, failures and tie lines are employed as decision variables. Next, the rush repair model is solved through a two-layer solution strategy. Simulated Annealing Algorithm and Genetic Algorithm are adopted respectively to solve the two layers. So that, the best rush repair strategy can be obtained. Finally, the efficiency of the proposed approach is demonstrated by testing on PG&E 69 node system.

In order to realize healthy communication and accurate information exchange between the charging tariff and control unit (referred to as TCU) and electric vehicles during charging service, the enterprise standard Q/GDW11709.2 of State Grid Corporation of China stipulates the communication protocol between TCU and charging pile. The protocol standard is the basis of the interconnection between the TCU and the charging controller (referred to as CTR), and the protocol conformance test is the precondition of ensuring the protocol implementation and healthy communication, one of the major components of protocol testing is generating test sequences. Based on the study of the communication protocol and mechanism between the TCU and the CTR, the finite state machine (referred to as FSM) model of Q/GDW11709.2 communication protocol is established, and the UIO sequence of each state in the state machine is calculated.

The propagation features of cascading failure in power system are basis for identifying critical branches and understanding the mechanisms of cascading failure. This paper defines propagation features as minimum sequential patterns with gap, and propose a method to identify critical patterns and branches. The proposed method is tested on IEEE 39-, 118-bus test systems. The case studies show the method can effectively identify critical patterns and branches from massive cascading failure simulation data. We also illustrate that the identification results help understand the features of power flow transmission and topology structure, and propagation correlations between branches.

Under the long-term influence of electrical load and natural environmental factors, the insulation performance of the insulator will gradually decrease, and deteriorated insulators with different degrees of deterioration will appear in the insulator string. The presence of degraded insulators can pose a significant risk to the safe operation of the power system. In order to study the influence of insulators with different degradation degrees on the voltage distribution of insulator strings in 500 kV transmission lines, this paper studies the distribution voltages of different positions, different numbers and different degraded insulators in 500 kV transmission line insulator strings based on finite element method. The simulation results show that the voltage drop of the insulator with a lighter degree of degradation is very low, and it is more difficult to identify by the distributed voltage. The deteriorated insulator has a certain lifting effect on the voltage of its adjacent insulator. Finally, the effects of different degrees of deteriorated insulators on the voltage distribution are studied by experiments. The conclusions are consistent with the simulation results.

With the rapid development of the electric vehicle industry, the charging interoperability problems are becoming more and more serious, and the requirements for charging reliability and safety are also getting higher and higher. AC charging is widely used because of easy to install and operate. Based on the existing AC charging system, this paper analyzed each charging state including the connection confirmation phase, charge ready phase, startup and charging phase, shutdown phase during the AC charging process. The interoperability test use cases of the vehicle and AC charging equipment were proposed. Through the construction of the interoperability test system, PWM signal parameters, the voltage of detection points, the transition between each state and the simulation of the abnormal faults for the vehicle and AC charging equipment were completed. Test results shown that the charging matching degree between the vehicle and the AC charging equipment using these protection strategies was improved. These effective measures minimized the safety risk and strengthen prevention capability of the charging process.

With the development of electric vehicle industry, the charging compatibility between charging facilities and electric vehicles has become the focus of attention of charging operators, charging facilities manufacturers, electric vehicle manufacturers and users. For electric vehicle DC charging, the communication protocol between the DC charger and electric vehicle plays an important role in the interoperability of chargers and vehicles, which is the basis for realizing charging information exchange and charging control, and is the guarantee for safe charging of DC electric vehicles. This paper firstly introduces the charging time-sequence and communication protocol between DC chargers and electric vehicles, then designs the DC charger protocol conformance testing system, which can verify the communication message of dc charging communication protocol, the timeout processing of BMS message, the consistence of charging output, and the abnormal processing of vehicle charging. Currently, the system has been widely used in the field test and laboratory test for DC chargers with significant effect.

Power-over-fiber (POF) technique is a commonly way to transmit power externally into the low-power remote modules in specific distributed measurement systems. This paper briefly analyzes the technical characteristics and application limitations of different implementations of In-band communication used in the POF system. Based on the analysis of the characteristics of key components such as laser diode (LD) and photoelectric converter (PPC), this paper proposes a closed-loop digital modulation method that takes the characteristics of photovoltaic voltage signals as feedback to control the drive current of LDs, and then the In-band communication link over the power supply fiber can be established. After that, this paper explains the architecture design, control circuit, feed-back demodulation design and closed-loop control software design of the POF system with In-band downlink capability. The specific test verifies that the proto-type realizes the downlink communication in POF system at 1 Mbps rate between the host and remote module which runs in industrial temperature environment. The method can meet the communication requirements of parameter configuration, sampling calibration and other functions in the industrial distributed measurement system.

For the radiated EMI (Electromagnetic Interference) noise generated by the inverter power supply, this paper studies the EMI noise suppression based on the electromagnetic shielding of the cabinet. By modeling the inverter power cabinet in the CST simulation environment, the electromagnetic shielding effect of the cabinet under different cabinet models is analyzed, and the simulation results are analyzed to optimize the shielding effectiveness of the cabinet. Finally, it is applied to the specific case and adopted rectification. After the suppression measures, the tested equipment can successfully pass the test and verify the effectiveness of the method. This paper provides a reference and basis for the research of radiated electromagnetic interference noise of inverter power supply and the later rectification of technicians.

In this paper, particle swarm optimization (PSO) and charge simulation method (CSM) are combined to calculate the three-dimensional electric field of sphere-plane electrode, rod-plane electrode and simplified high-voltage insulator string. The optimization variable of PSO is the coordinate or charge quantity of the simulation charge, and the fitness function is the potential error of the matching point. In the calculation of the sphere-plane electrode, the calculation results verify the effectiveness of PSOCSM; the comparison with GACSM highlights the advantages of short calculation time and high precision of PSOCSM. In the calculation of the rod-plane electrode, a method to reduce the optimization variables of PSO is proposed to further improve the computational accuracy and optimization efficiency of PSOCSM. In the calculation of the rod-plane electrode and the high voltage insulator string, the comparison results of GACSM and PSOCSM reflect the excellent optimization performance of PSO. The research results provide a new calculation method for electric field calculation of high voltage electrical appliances.

The detection of environmental change in transmission channel based on high-resolution satellite remote sensing is the emerging trend in the operation and maintenance of power grid. To improve the precision, a change detection method based on the deep learning was put forward in this paper. Firstly, by the improved change vector analysis algorithm and grey level co-occurrence matrix considering the neighborhood information, the spectral and texture changes of images at different times were calculated. Moreover, the most probably changed and unchanged zone samples were extracted through setting the adaptive sampling interval. Additionally, the modified deep convolutional neural network model Inception-v3 was constructed and trained. The in-depth features in changed and unchanged zone were extracted, and the changed zone was distinguished effectively. The results showed that the proposed method could effectively extract the environmental change in transmission channel, and the accuracy reached over 92.5%, which was obviously superior to the contrast methods.

Parameters identification of power system inter-harmonic is research emphasis recently, because it can be seriously interfered by nearby harmonic leakage. The sub-harmonic lower than power frequency is not only disturbed by power frequency component, but also interfered by negative frequency component itself. A variety of analytical methods are used for acquiring the parameters of the signal but difficult to eliminate various confounding factors adequately. Therefore, as sub-harmonic component can be interfered by both negative frequency component and power frequency component, an improved low frequency spectrum correction model was established, and different frequencies of sub-harmonics were analyzed with scanning way. Simulation results show that the proposed method for identification of sub-harmonic parameters has higher accuracy, small computation and is easy for project implementation.

Video surveillance systems play an irreplaceable role in power scene monitoring. However, in the process of image acquisition, video surveillance equipment will inevitably have various external interference factors. This paper proposes an effective method for video shake detection. This method incorporates the classical BOW (bag-of-words) model into the traditional projection method to get the acquired feature representation in a higher level and make it more robust. In addition, we focus on the local characteristics of the image and use a block operation. The experimental results show the effectiveness of our method.

It is essential to calculate the height of lightning rods in order to make them function efficiently. But sometimes the calculation can be complex. In order to calculate the heights of lightning rods, we follow the principle of “Centering on the axis line and firstly focus on the upper level”. With the help of tools like Borland C++ Builder, a computing software for calculating the height of lightning rods was designed, and it can cover the calculations of the height(s) of one single lightning rod; two lightning rods of the same height; and two lightning rods with different heights, using polygon method. It can also deal with the calculation of the height of one single lightning rod using rolling ball method. The main points of the calculation, the main program code of the software and several examples are shown in the paper. After comparison and verification, the results given by the software are reliable. The use of the software can greatly reduce the burden of calculation during the design of lightning rods.

The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words. Aiming at the low resolution problem of fine-grained fittings image of trans-mission line, an image super-resolution reconstruction algorithm based on compressed sensing is proposed. The K-SVD (K-Singular Value Decomposition) algorithm is used to implement sparse representation according to the theory of compressed sensing. The reconstruction is performed by OMP (Orthogonal Matching Pursuit) algorithm. The proposed algorithm has good de-noising effect and shortened processing time. The fine-grained fit-tings image that has correlation with the reconstructed image is trained to enhance the reconstruction effect and is used for high-quality recovery of the fine-grained fittings image of the transmission line. The simulation results verify the effectiveness of the proposed algorithm, and the reconstructed image has a better improvement in subjective visual effects and objective evaluation indicators.

The existing work of operation and maintenance for substations is mainly realized by manual on-site inspection. It is repeated and cumbersome, and costs lots of time. With the rapid growth of the number of substations, the increase in the number of operation and maintenance personnel cannot keep up with the development speed of the power grid equipment, which brings great work pressure to the operation and maintenance of substations. In order to solve the problem, this paper proposes a design and implementation method of centralized operation and hierarchical management system for substations. The system collects the information in the substations and sends it to the center, and combines the information collected by the center and then sends it to the upper center to achieve the goal of centralized operation and hierarchical management for substations. The system has been used in practical applications and it greatly reduces the load and time of operation and maintenance work. It improves the management level of substation operation and maintenance and makes it more scientific, standardized and reasonable.

Distributed line fault diagnosis system and information release technology are of great significance to grid security operation and maintenance. Combined with the development and application of the actual system, a multi-server based distributed fault diagnosis information security releases technology is proposed. From the source of device data to the end of publishing terminal, take various technical means such as security output, secure access, and network isolation to achieve information security. Real-time and consistency of data is achieved through multi-server deployment, physical isolation of servers, and real-time synchronization of heterogeneous databases. The principles of network security access and network isolation are described in detail. The security interaction between the monitoring center server and the mobile App server, information synchronization, module design and implementation methods are successfully certified by the China Electric Power Research Institute and implemented in Shanxi Province. High-voltage AC 1000 kV transmission line running on the network.

Aiming at the problems of large workload, low efficiency of distribution network assets management and the inability of traditional technical which means to meet the requirement of lean asset management brought by the rapid expansion of distribution network scale, a lean management method for distribution network assets operation and maintenance based on RFID is proposed by studying the RFID tags direct scanning technology, internet of things technology, big-data processing technology. The proposed method has been applied to the lean management system for distribution network assets operation and maintenance. The pilot application in some cities indicates that with the traditional methods of power network assets management, the proposed method can achieve the functions of real-time access, maintenance and data verification of distribution network assets, ensure the consistency, real-time and synchronization of distribution network assets information, reduce the workload of distribution network assets information management, and can significantly improve the accuracy of asset information and the ease of control in the process of operation and maintenance.

Infrared Intelligent detection is the key research direction of infrared fault detection of power equipment. However, different types of equipment and different processing purposes lead to a variety of fault detection methods. If the manual method is used to classify the equipment, the efficiency of fault detection will be greatly reduced. In this paper, based on convolutional neural networks, based on RGB and HSV color space conversion, a classification model suitable for infrared images of power equipment is constructed. Firstly, the structure characteristics and training process of CNN are introduced. After that, based on RGB and HSV color space conversion, the infrared image of substation equipment is processed, and the target area of suitable size is extracted to establish network training and test set. Finally, a CNN-based infrared image classification model is established, and its good applicability is verified by case analysis.