Advances in Energy Power and Automation Engineering

Geothermal energy can help improve the energy mix and reduce carbon emissions. CO2 can be used as a heat exchange medium in the process of geothermal development, and at the same time, realize the geological storage of CO2. In this chapter, an enhanced geothermal system (EGS) with coupled thermal–hydraulic–mechanical (THM) containing discrete fractures is developed. Using the sensitivity analysis method, 13 formation parameters controlling the heat production performance of CO2−EGS were studied with thermal breakthrough time and net heat production rate as target parameters. It is found that the most important parameters affecting the heat production performance of CO2−EGS are matrix permeability and fracture aperture.

Accurate wind speed prediction through numerical weather prediction (NWP) can reduce the operating cost of wind farms. To satisfy the demand of wind forecasts of high temporal and spatial resolution for power prediction at wind power stations in Shanxi province, surface wind field forecasts at the new energy stations from the global forecast system (GFS) model are temporally and spatially downscaled from September 1, 2021, to August 31, 2022, in this paper. Results showed that the performance of the downscaled forecast product based on the optimal frequency bias (OFB) algorithm is better than that of the original model forecast for different initial times, lead times, ground levels, and wind speed grades. The threat score (TS) of surface wind speed forecast fluctuates periodically with forecast lead time. In general, 14 PM corresponds to the peak of the TS time series, while 02 AM corresponds to the trough. The TS improvement of the downscaled products initialized at 00 UTC is greater than that initialized at 12 UTC. Furthermore, the improvement is more obvious for wind speed levels that are closer to the ground.

In the collection of local electrical signals, electrical equipment is in operation, and in the detection process, there will be a certain degree of noise interference, electromagnetic interference, or other interference. So the original signal received will be biased due to interference. Therefore, how to solve the noise processing of the original local emission electrical signals. In this paper, we thoroughly investigate noise reduction techniques based on wavelet threshold methods. We give an overview of wavelet theory and introduce the basic principle of the wavelet transform and its application in signal processing. In the experimental part, the methods of Dmey wavelet global default threshold denoising, Haar soft SUR threshold denoising, and DB3 wavelet fixed threshold are used to denoise and analyze the noise reduction effect under different threshold treatment methods.

With the goal of “double carbon” and the deepening of China’s power system reform, the operating characteristics of the power system are more complex and variable. The demand for frequency control of traditional thermal power units and hydropower units is growing. Load frequency control (LFC) is one of the important means of frequency control in the power system. Its primary goal is to keep the system frequency within zero steady-state error. A neural network proportional-integral-derivative (PID)-based energy storage participation LFC strategy is proposed for the load frequency problem of two types of units. First, the traditional closed-loop LFC models of thermal and hydroelectric units are established according to the frequency response characteristics of the traditional units; then, the energy storage system used for the frequency regulation of the power system is selected, and the corresponding frequency response model is established. Finally, an LFC controller design method based on a neural network PID algorithm is studied, and the simulation results show that the proposed neural network PID control method has better response characteristics compared with the traditional PI control.

With the implementation of the dual carbon policy, user-side energy management is an important carbon reduction initiative. Load identification is an important customer-side energy management method, and after obtaining the voltage and current information of the customer side, the hidden information is mined by machine learning and other methods. However, the sample entries on the customer side are small, and it is difficult to train a conventional network with better results. Therefore, this chapter proposes a small-sample load identification method that combines the squeeze-and-excitation (SE) attention mechanism. It first constructs colored V-I trajectory maps based on voltage and current signals of conventional users and then constructs a neural network model for trajectory map classification. The SE attention mechanism is added to the network so as to give more attention to the channels containing more information, thus improving the classification effect. Finally, the effectiveness of the proposed method is verified by classifying appliances using the WHITED dataset. The proposed method plays an important role in small sample load recognition information mining.

This chapter proposes a planning method based on an improved solution algorithm to minimize the energy cost and thus realize the optimal configuration of the system for example a distributed integrated energy system (IES) in a certain region. The model takes into account the technical and economic parameters of the equipment, the user’s multi-energy load profile, the minimization of the system energy cost as the goal, and the comprehensive consideration of the equipment capacity configuration as well as the economic operation. The uncertainty analysis of the equipment parameters is solved by adopting the Latin hypercube sampling (LHS) method for the key equipment parameters, and the Monte Carlo algorithm is improved by combining with the convex optimization method to solve the above planning problems, and the global optimal results are found. The optimization results show that the proposed method can improve the operation efficiency of model solving and effectively reduce the operation cost of the system, which verifies the feasibility of the method.

CZTSSe is a promising clean photovoltaic material; however, in fact, the power conversion efficiency (PCE) of CZTSSe solar cells is still far below the maximum theoretical efficiency, and one of the important reasons is the crystallographic factors that limit the performance of CZTSSe. In this chapter, SCAPS-1D is used to simulate the performance of CZTSSe cells with MoS2 as the HTL layer, focusing on the simulation of the thickness ratio, defect density of MoS2, WS2/CZTSSe, and CZTSSe/MoS2 interface defect density. The optimal thicknesses of the CZTSSe and MoS2 are determined to be 400 and 1600 nm. It is found that the interface defects of the buffer layer and the absorption layer have a significant impact on the performance of the battery, and the expected reasons are given. Finally, the optimized PCE can reach 22.31%. This work may be helpful for the application of MoS2 in CZTSSe solar thin-film cells, and make a little contribution to the continuous development of CZTSSe solar cell research.

Kesterite materials such as CZTS are widely found in nature and have the advantages of nontoxicity, high absorption coefficient, direct bandgap, etc. These characteristics are conducive to the production of solar cells. Solar technology has seen some success in recent years, but there is still a big gap between the actual conversion efficiency and the ideal conversion efficiency, and the main problem lies in the defects of the CZTS layer and CdS/CZTS interface layer. Copper iodide (CuI) has better photovoltaic characteristics, which can be used as a potential solution to this problem. Therefore, different from traditional solar cells, a CuI layer is added as a hole transport layer (HTL), and the SCAPS-1D program is used for simulation. Adjust the thickness of the CZTS layer and CuI layer, defect density of the CZTS layer and defect density of the CdS/CZTS interface layer, and other different variables to optimize the Cu/AZO/i-ZnO/CdS/CZTS/CuI/Au structure. The battery structure was optimized through power conversion efficiency (PCE), short-circuit current (JSC), open-circuit voltage (VOC), and filling factor (FF), and the efficiency was finally increased to 21.26%.

In the twenty-first century, with the efforts of scholars at home and abroad, the related technologies of wind power generation are being improved. In this chapter, combining the flow control method of wind turbines and the resistance to wind and sand erosion and wear, this chapter investigates the effects of two flow control methods, namely, the leading-edge small wing and the leading-edge cylinder, on the aerodynamic performance and resistance to wind and sand erosion and wear of wind turbine wing profiles. Research has shown that both flow control methods can enhance the aerodynamic performance of airfoils while enhancing their resistance to wind and sand erosion and wear. Compared to the leading-edge small wing control airfoil, the leading-edge cylindrical control airfoil has a better anti-erosion and wear effect, but the improvement in the effect of aerodynamic performance is opposite.

Although Zn metal offers low cost, high safety, a high theoretical specific capacity of 820 mAh g−1, and a smaller redox potential (−0.76 V versus SHE), it encounters issues like dendrite rampant generation, corrosion, and passivation reaction when utilized in the anode of aqueous Zn-ion batteries. Together, these issues reduce the cycle life of the battery and the coulombic efficiency. In this chapter, a bifunctional coating is constructed on the surface of the Zn anode by repeated spin-coating twice. The coating is composed of two layers, one layer on the surface of the Zn electrode is a tannic acid (TA) coating, which uses the chelation effect of TA and zinc metal to anchor Zn2+ on the cross-linking network of TA molecules, which limits the two-dimensional deposition of Zn2+, thereby producing a uniform nucleation site, avoiding the “tip effect”. The polyurethane (PU) coating that touches the electrolyte layer acts as a solid barrier to keep water molecules from reaching the surface of the Zn electrode because of its strong water-repelling properties, preventing both the hydrogen evolution reaction (HER) and severe corrosion reaction from happening. The Zn@PT//Zn@PT symmetrical battery exhibits exceptional cycling stability, enduring over 1200 h at 1 and 0.5 mAh cm−2, with minimal overpotential. Despite the rise in a current density to 5 mA cm−2, the cycling performance of the full cell remains stable for over 500 h, surpassing that of Zn//Zn symmetrical cells. The Zn@PT//MnO2 cells possess a high specific capacity of 271.6 mAh g−1 at a current density of 0.2 A g−1.

A novel method is proposed to analyze the reasonable utilization rate of renewable energy considering the cost of adjustable resources in provincial area. The formula for calculating the utilization rate of renewable energy in provincial region is quantitatively given by combining the power supply load of the power system in the target region and the operating constraints of non-hydro conventional units. And then the analytical expression for the utilization rate of renewable energy based on the cost of consumption is proposed by combining with the provincial power supply–demand balance model that takes into account the output of renewable energy. The results of one case study show that the renewable energy utilization rate gradually increases with the growth of flexibility resources, but the effect of renewable energy consumption per unit of new flexibility resources gradually decreases, and the reasonable renewable energy utilization rate in the analyzed area in some year is 0.9886.

Partial discharge (PD) detection plays a pivotal role in ensuring the integrity and reliability of electrical power systems. PD events serve as precursors to insulation breakdown, posing significant threats to equipment safety and operational continuity. Traditional PD detection methods have exhibited limitations, particularly in liquid insulation environments. This chapter presents an interferometric method for detecting PD in liquids. Employing a setup based on interferometry, PD in the liquid sample is induced by the voltage from a lightning surge generator, and the resulting distorted interference patterns are captured using a CCD camera. The experimental results show that the peak value of the phase after PD is larger than that before PD, and the peak value of the phase distribution increases accordingly with the increase of the voltage. This study highlights the direct relationship between the applied voltage and the phase distribution generated within the interferogram, which provides a new idea for condition monitoring and fault diagnosis of liquid insulation systems.

Household energy consumption continues to rise as the living standards of residents improve, making household carbon emissions a major contributor to global carbon emissions. Aiming at a low-carbon management strategy for the virtual power plants (VPPs), this paper estimates household carbon emissions based on dynamic carbon emission factors that can accurately reflect differences in carbon emissions generated by electricity, which can accurately reflect the difference of carbon emissions generated by electricity and heat consumption in each period. On this basis, a carbon demand response mechanism was established to guide users to adjust their energy consumption plans according to the carbon emission intensity of energy consumption in different periods. The coordinated load plan influences the VPP scheduling plan and achieves the interaction between supply and demand. According to the simulation results, compared with the traditional integrated demand response (IDR) mechanism, the carbon emissions of this method are reduced by 9.3%, and the energy supply cost is reduced by 18.9%, which shows that the CDR mechanism proposed in this paper can effectively reduce the carbon emissions and energy supply cost of the system.

To achieve the economic, low-carbon, and high-efficiency cooperative operation of gas–steam–power system (GSPS) in iron and steel enterprises, this chapter proposes a multi-objective optimal scheduling method that takes into account the exergy efficiency of GSPS. First, the analysis approach of electricity equivalent is used to simplify the calculation of the exergy efficiency, and a low carbon emission reduction method based on exergy efficiency is proposed for the GSPS, which can achieve the dual goals of reducing system carbon emissions and reducing energy consumption. Then a multi-objective scheduling model is constructed to maximize the exergy efficiency of the system and minimize the operating cost. Finally, the Pareto frontier solution is obtained by using the traversal weight method of solving, and the optimal decision solution is determined by combining with the TOPSIS method. The effectiveness of the proposed method is verified by simulation examples, which can provide a guiding program for the sustainable development of iron and steel enterprises.

The short-term forecast of power load is of great significance to the planning and development of the power industry. In this chapter, we propose a new method to enhance the performance of predictive models by combining subtraction mean optimization (SABO), particle swarm optimization algorithm (PSO), and extreme learning machine (ELM). Through careful parameter adjustment and optimization process, this study successfully demonstrated the effectiveness of SABO and PSO algorithms in optimizing ELM parameters, which greatly improved the application performance of the model in practical prediction tasks. In addition, the method in this study provides a feasible solution for processing complex data sets, enhancing the adaptability and stability of the model in the face of data changes. Future work will explore the potential of this approach for other types of machine learning tasks and on larger data sets. The proposed method was evaluated using the 2021 load data of the PJM public dataset and the 2022 full-year data of an industrial park in Liaoning province. The SABO-PSO-ELM method is compared with other mainstream methods (BWO-ELM). The statistical analysis shows that the proposed method has better prediction accuracy on the four standard scales of MSE, MAPE, MAD, and NRMSE, which reflects the advanced nature of the method.

Compared with traditional single storage technologies, a hybrid energy storage system (HESS) combines various storage methods, utilizing the advantages of multiple techniques and compensating for the shortcomings of a single storage technology. It is one of the effective ways to address the intermittency issues of renewable power output. Addressing issues of rapid fluctuation and randomness of renewable power, this paper proposes an optimization configuration method of a power-energy hybrid storage system (PEHSS) for renewable power plants (RPP). The proposed method decomposes and reconstructs the original output data of RPP, obtaining the high-frequency fluctuation information and low-frequency steady-state information. Subsequently, the high-power energy storage system (HPESS), such as flywheels and super capacitors (SC), can be configured with the high-frequency fluctuation information, which contributes to the suppression of power fluctuations for RPPs. Then the capacity of the energy storage system (ESS) can be calculated with the low-frequency steady-state information to achieve within-day peak shaving for RPPs. This research overcomes the shortcomings of traditional planning methods in multi-time scale configuration for HESS. The case studies verify the effectiveness of the proposed method.

Rural microgrids are characterized by a close connection between industrial production and energy demand, which can play an important role in optimizing the flexible scheduling of the distribution market. This chapter proposes an integrated optimization model and method for production management of rural microgrids, considering photovoltaic output, in which ground source heat pumps are introduced to provide hot and cold energy for rural microgrids to optimize the energy strategy of rural microgrids; finally, the feasibility and effectiveness of the method are verified through an example analysis.

Identifying the equivalent network parameters of the transformer winding is essential for the interpretation of the frequency response analysis (FRA) and driving-point admittance (DPA) data. This chapter presents a method for parameter identification that uses the DPA data and the improved whale optimization algorithm (IWOA) to invert the equivalent network parameters of the transformer. First, the DPA measurement model and its state-space equation of a double-winding transformer were established. Next, an objective function was constructed by the resonant amplitudes of the reference DPA curve and that of the estimation DPA derived by the above-mentioned state space equation. Then, an improved whale algorithm was proposed by updating the population generation method, the convergence factor, and the inertia weight of the whale algorithm. Finally, the validity and advantage of the parameter recognition method, based on the objective function and the IWOA, were proved by identifying the parameters of the simulation model and the comparison with the particle swarm optimization (PSO) and genetic algorithm (GA) algorithms.

As an important energy conversion equipment in the power grid, power transformers have a complex temperature field, which is related to their own operation life and the stability of the power system. Therefore, accurately obtaining the characteristics of the temperature field and hot spot temperature of transformers has important practical value. At present, there are two methods for constructing the temperature field of transformers, including model driven and data driven. However, existing data-driven methods couple temporal and spatial information when constructing networks, resulting in imbalanced spatiotemporal modeling and making the network training process difficult. Therefore, we propose a transformer temperature field prediction method based on spatiotemporal decoupling training, including spatial feature vector quantification and temporal feature modeling. It can balance the spatiotemporal modeling process, reduce the difficulty of model training, and effectively improve the accuracy of transformer temperature field prediction.

The substation line is usually located in the high-voltage area of the system. Although the general lightning protection reform measures for it will strengthen the lightning resistance level of the line and improve the operation reliability, they will also increase the lightning intrusion wave of the substation, which will have an impact on the insulation coordination of the equipment in the station to a certain extent, especially affecting the characteristics of the lightning arrester, resulting in the increase of overcurrent and heat, and then shortening its service life. In summary, taking a 330 kV substation and its line segment in a high-altitude area as an example, ATP-EMTP (the Alternative Transients Program—the Alternative Transients Program) was used to establish a simulation model, and the influences of different poles and towers in the line segment on the current and overvoltage of the arrester in the station were, respectively, explored. Based on this, considering the condition of the line body and the insulation cooperation with the equipment in the station, the differentiated lightning protection transformation of the line segment is finally realized.

In addressing the automatic recognition and classification of equipment in the construction of smart substations, we propose a method based on multi-view inspection images for automatic classification of substation equipment. This method utilizes the YOLOv8 object detection algorithm for automatic classification of substation equipment. Through multi-view inspections of substation equipment to capture images from different angles, a segmented substation equipment sample set is used to train the YOLOv8 algorithm. The collected data is preprocessed, and the trained YOLOv8 algorithm is then used to classify and recognize images, achieving automatic classification of substation equipment. Experimental results demonstrate the successful detection of various types of substation equipment, including disconnectors, racks, insulating porcelain bottles, current transformers, surge arresters, and circuit breakers. At a threshold of 0.5, the average recognition accuracy for all classes is 0.502, indicating the application potential of the YOLOv8 algorithm in substation equipment recognition tasks. This method can provide certain technical support for the construction of smart substations.

This chapter explores the problem of decoupling distribution networks through an improved distributed parametric long-line decoupling method. The traditional long-line decoupling method has certain limitations when applied to the distribution network, mainly due to the short-line characteristics in the distribution network, which cannot fully utilize the transmission delay decoupling. In response to this problem, this chapter proposes an improved line decoupling method. This improved method introduces compensation capacitance to increase the wave propagation time without changing the line length, thereby realizing the decoupling of lines in the distribution network. And by building a model for simulation analysis, the effectiveness of the method is verified, providing new ideas and methods for solving the decoupling problem of distribution networks.

The primary objective of this study is to address the issue of complete AC power loss of safety class motors within nuclear power plants in the event of an open phase fault occurring in the auxiliary transformer. The analysis commences by examining the mechanism responsible for inducing voltage on the primary side following an open phase fault in transformers featuring d-type windings on the secondary side. It employs positive sequence and negative sequence equivalent circuit models for motors to assess voltage and current behaviors during this fault. The study then shifts its focus to how the voltage of the auxiliary transformer and the safety-related motor respond to variations in grounding resistance and auxiliary transformer load ratio. The analysis reveals that when the grounding resistance falls within the range of 500 to 2500 Ω, the safety-related motor in the unit may lose its entire AC power supply. Moreover, if the grounding resistance exceeds 2500 Ω and the auxiliary transformer bears even a minimal load, the safety-related motor will lose all AC power due to a significant imbalance. In conclusion, the chapter proposes a solution by introducing a negative sequence phase voltage imbalance protection relay. This approach ensures that, in the event of an open-phase fault, there will be no adverse impact on the safety-related motor. In other words, it will prevent equipment damage or false tripping and guarantee that the safety-related motor does not lose its entire AC power supply, thus contributing to the overall safety and stability of nuclear power plant operations.

In this chapter, the vibration response characteristics of NREL’s 15 MW floating wind turbine under the environmental combination of wind shear incoming flow, turbulent wind, and regular and irregular wave conditions are investigated by numerical simulations with OpenFAST software. It is found that the wave load mainly affects the fore-aft shear at the base of the tower, where the irregular wave has the most significant effect on the response value; the turbulent wind significantly increases the volatility of the side-to-side shear at the base of the tower. The environmental contour method is used to predict the ultimate response of the unit, and the study shows that the wind turbine will show the ultimate response under specific conditions, which are as follows: under the conditions of wind speed of 10 m/s, significant wave height of 5.36 m, and spectral period of 11.61 s, the fore-aft shear at the tower base of the wind turbine will show the ultimate response; under the conditions of wind speed of 25 m/s, significant wave height of 3.95 m, and spectral period of 6.23 s, the side-to-side shear at the base of the tower will show the ultimate response. The side-to-side shear at the base of the tower shows the limit response for the case of wind speed 25 m/s and spectral period 6.23 s.

The virtual inertia control (VIC) of converter-interfaced generations (CIGs), which is originally applied to provide inertia support, will also affect system rotor-angle stability. This chapter proposes to control the virtual inertia constants coordinately such that sufficient inertia support is achieved, and meanwhile, the small signal rotor-angle stability can be enhanced without applying damping control strategies. Numerical studies in the IEEE 68 bus system have demonstrated that the proposed control strategy is effective enough.

To achieve the dual carbon goal, China has decided to develop energetically clean energy and will develop a new energy system that uses low inertia to reduce carbon emissions. This new technology will have a significant impact on the security and stability of the system. In order to accurately identify system risks, this chapter presents an evaluation study on the influence of inertial errors on the risk of new power supply systems. An evaluation index system is constructed in three dimensions: the generation, load side, and energy storage side. To measure the evaluation results, the AHP method and the cloud model are selected. This suggests that inertial errors have a significant impact on the risk of new electrical systems.

In this chapter, the algorithm is used to locate the weak link of the power system, and then the strategy of voltage and reactive power optimization in the low-voltage area is put forward. Under this strategy, the compensation degree can be located quickly, and the voltage can be optimized so that the coordination of reactive power compensation capacity and compensation position can be considered. Through the simulation analysis of the actual power grid, the control strategy proposed in this chapter has a good control effect on the regional voltage stability, and provides a guiding basis for the economic dispatching of the power grid.

Analysis of the discharge current of lightning arresters under typical lightning strikes can be carried out to determine the aging test of the lightning arrester and accurate life assessment by providing a waveform basis. But also to identify different types of lightning strikes, and to carry out targeted lightning protection measures. In this chapter, a PSCAD electromagnetic transient simulation model is constructed for a practical double-circuit 10 kV cable-overhead transmission line-cable typical configuration. The monitoring range of the lightning arrester discharge current monitoring device on the terminal tower under various lightning strike types is analyzed, along with the impact of lightning current amplitude and strike location on the waveform of discharge current at the terminal tower arresters. Additionally, the range of discharge current waveforms at terminal tower arresters under different lightning strike types is statistically compiled. The work in this chapter will provide a database for lightning arrester aging tests and lightning strike type identification.

Nuclear power plants involve numerous systems and equipment, complex system processes, and strict operating conditions. More technical problems in the field cannot be tested and verified directly on the unit, and the cost and price of offline physical experiments are also higher, which is not conducive to the analysis of the root cause of the problem and to determining the solution. The computational modeling simulation technology in the creation of numerical models and the simulation of the problem simulation and so on has more advantages. Early mainstream modeling and simulation platforms are mostly imported from abroad, such as MATLAB and Simulink. With the continuous maturity and wide application of domestic modeling and simulation platforms, they are increasingly used in aerospace, ship, vehicle, nuclear energy, and other fields. The chapter introduces the typical application of the MWorks domestic modeling and simulation platform in some nuclear power plants to introduce the basic usage and precautions of the platform in the analysis of on-site problems and formulation of solutions in the field of technological retrofit.

To enhance the stability and economic efficiency of power distribution systems with high penetration of renewable energy sources, this paper proposes an optimization model for active distribution network scheduling based on Stackelberg game theory. By utilizing the Stackelberg relationship between the active distribution network and the load aggregator (LA), a day-ahead game model is established to achieve peak shaving and valley filling in the distribution network while considering the economic interests of both master and slave. The feasibility of the proposed model is verified through case study analysis.

For oil-consuming electric power equipment, their protection and condition monitoring are dependent on the real-time test of its oil’s various performance, for which impedance spectroscopy (IS) provides a potentially efficient technology. Therefore, the current situation and prospect of IS and its application in Electric Power Equipment Oil (EPEO)’s performance test are reviewed in this paper. First, IS is introduced from new classifications of univariate IS and multivariate IS, the current problems in EPEO’s performance test are summarized, and for solutions to these problems, the principle of why a matter system’s various properties can be tested by IS and IS’s advantages and application potentials are analyzed. Then, IS’s development status in impedance spectrum’s measurement and acquisition, analysis and processing, and industrial application is summarized, and its implementation scheme and key problems in EPEO’s performance test, including the construction of impedance spectrum’s measuring system, the analysis and processing of spectrum data, and the representation and derivation of EPEO’s performance, are also provided. Finally, it is pointed out that (1) the pursuit of high-precision, rapid, miniaturized, and multivariate impedance spectrum measuring technology and instruments; (2) for impedance spectrum analysis and processing, the mining of in-depth information, construction of new object models, processing of the impedance spectrum under nonlinear or even unstable conditions, and the analysis of multivariate impedance spectra; (3) the development of industrial applications in depth and breadth are IS’s future trends.

In recent years, with the impact of human activities on the natural environment and the periodic change of global climate, the number of extreme natural disasters such as typhoons, ice and snow, and earthquakes has been increasing. This study introduces a fault rate assessment model of distribution network lines based on equipment health under wind and rain disasters, which includes the following steps: First, the stress of distribution network wires and pylons under typhoon and rainstorm is analyzed, and the failure rate model of wires under typhoon rainstorm disaster is obtained based on the pressure of wires. Then, the failure rate evaluation model of the distribution network line with equipment health is obtained by combining the failure rate model of typhoon, rainstorm, and equipment health. Finally, a case is given to verify the effectiveness of the proposed model.

Aiming at the impact of traction network voltage on high-speed railways operating under different conditions, such as traction and braking, resulting in the rise and fall of traction network voltage, which has a negative impact on the safe running of trains, a traction network active power and harmonic current compensation control strategy based on on-board energy storage is proposed. It can effectively suppress the voltage fluctuation of the traction network caused by different operating conditions. The regenerative braking energy is greatly utilized and suppresses harmonic overvoltage. Firstly, the topology circuit of the vehicle energy storage system is established, and the operating mode of the system is analyzed. Then, two kinds of network pressure fluctuation mechanisms are analyzed, and the compensation principle is studied. Secondly, the method of voltage fluctuation compensation of the traction network and its system topology is proposed. Finally, the feasibility and correctness of the scheme are tested through the establishment of a simulation. This method can effectively suppress voltage fluctuations in traction networks under different conditions, as well as harmonic currents.

Traditional centralized optimal scheduling faces issues such as complex control, low reliability, and information privacy. Compared to centralized control, distributed control has advantages such as greater flexibility and reliability. Therefore, this paper proposes a distributed optimal scheduling strategy for distribution networks with high-proportion photovoltaic (PV) integration. Firstly, based on the modularity index and cluster self-consumption capacity index, the genetic algorithm is used to partition the distribution network clusters. Then, aiming at minimizing network loss costs and curtailment costs, a distributed optimal scheduling model for the distribution network is established and solved using the alternating direction method of multipliers (ADMM). Finally, using the IEEE 33-node distribution system as an example for simulation verification, the proposed strategy can effectively enhance the PV consumption capacity of the distribution network, reduce system network losses, and improve the power quality of the system.

In the preliminary design calculation of the magnetically saturated controllable reactor (MSCR), the finite element simulation, although the accuracy meets the requirements, the complexity of the model is more complicated, and the time spent is costly. By observing the magnetic field distribution of the finite element model and considering the structural characteristics of the MSCR, the mesh is dissected, and the equivalent flux tube method is used to establish a magnetic network model considering the magnetic leakage effect. Based on the similarity between the magnetic network and the electric network, the nodal method is used to establish the nodal magnetic potential equation for solving, and the operating current of the MSCR can be obtained from Kirchhoff's law of magnetic potential and Ampere's law of loop.

In order to solve the problem of large volume and high weight of phase shifting transformers, which are necessary components in multi-pulse rectifiers. A parallel 18-pulse rectifier based on an electrical and electronic transformer is proposed, consisting of an electrical and electronic converter and a high-frequency multi-pulse rectifier. By using an electrical and electronic converter, the AC power frequency can be converted to a high frequency. Then, the phase-shifted through a phase transformer to form three sets of three-phase rectifier bridges for rectification. This chapter studies the working principle of power electronic transformers and formulates management strategies based on their working principle. After analysis, the coupling between input and output voltages of power electronic transformers is obtained through the operating mode under high-frequency conditions of rectifier bridges. The simulation results show that replacing traditional power frequency transformers with electronic transformers reduces the transformer volume by two-thirds, ensuring stable power quality and confirming the feasibility and accuracy of the theoretical analysis.

Aiming at the challenges of tightly coupled and difficult-to-change business applications of power information terminals of new power systems, this chapter proposes a microservice-oriented business disassembly and combination method for power distribution terminals. First, the electric power business organization process is portrayed in the form of electric power functional components by business, and the levels and types of electric power functional components are modeled to give the electric power business decomposition model. Then, the coupling model between functional components and component attributes is established, and based on which, the power business splitting constraints, power functional component performance evaluation indexes are given, and the business splitting is completed. At the same time, the open architecture is used to combine the functional components into modules with higher hierarchical granularity, and changes in the business requirements are localized to the specific modules to save development cost and cycle time, and to improve the scalability of the business applications. Finally, we analyze the impact of the splitting scheme and modular combination strategy on the coupling and scalability of electric power business applications through examples, and compare the solving effects of different architectures, so as to provide means and basis for analyzing the reasonable construction of organizational splitting and modular combination of electric power distribution terminal business.

Under the background of “carbon peak and carbon neutrality” proposed by China, the power market trading has flourished, and the capital has participated in the trading of distributed energy as an operator. In order to ensure that the data of users participating in the electricity market transaction has credibility, and lay a foundation for the development of the electricity market trading system and the issuance of green certificates, a trusted metering terminal for distributed power supply is proposed. By integrating lightweight acquisition, power optimization, data encryption, and blockchain technology, it provides secure data trust support for operators and distributed power users to participate in power market transactions.

To tackle the problem of reduced accuracy of model prediction current control (MPCC) response of the permanent magnet synchronous motor (PMSM) caused by parameter mismatch, this paper proposes an MPCC method with improved duty cycle. First, the duty cycle is added to the single-vector MPCC, and a duty cycle control model is established; secondly, a motor parameter identification algorithm is designed based on the Adaline neural network and least mean square (LMS) algorithm; and finally, identification parameters are applied to the duty cycle control to achieve the real-time update of model parameters. Through the system simulation verification, the algorithm reduces the current fluctuation and steady-state error when the motor parameters change, and effectively improves the current response accuracy.

In recent years, electric vehicles (EVs) have gained popularity due to their low energy consumption and simple structure. However, the issue of limited driving range has not been effectively addressed. In order to alleviate the “range anxiety” phenomenon of drivers, this paper proposes a hybrid neural network model based on gated recurrent unit (GRU) and deep neural network (DNN) to accurately predict the energy consumption of EVs. The driving data is divided into time series and non-time series data based on their characteristics. It then employs GRU network to train the time series data, integrating the fitted values with non-time series data within a DNN network for further learning. Processing time series and non-time series data separately reduces computational burden and improves model performance by allowing feature engineering to focus more on the unique properties of each data type. The proposed hybrid neural network makes full use of the advantages of different network structures in learning characteristics, more comprehensively captures various influencing factors of electric vehicle energy consumption, and improves the learning ability of the model. The comparative results illustrate that GRU-DNN can obtain higher prediction accuracy, and the convergence speed is better than the comparison algorithm.

In order to solve the problem of contact failure caused by insufficient push force when avionics connectors are assembled with push tools, based on the MCU, a system that can monitor the push force of the push tool is designed. The sampling frequency of the system is 2 Hz, and the push force after detection and processing is stored in the SDHC card and output to the screen for display. The test shows that the maximum bias of the system is 0.43%, with high precision, and the push force that meets the requirements can be applied according to the pressure prompt of the LCD screen.

To enhance the dynamic response property and anti-interference capability of the permanent magnet synchronous motor (PMSM) system, the article proposes a sliding mode controller based on the Super-Twisting Algorithm (STA), which has been successfully applied to the speed control of the PMSM system. The stability and finite-time convergence property of this control algorithm were proven. To prove the effectiveness of the proposed STA algorithm, the designed super-twisting sliding mode controller (STSMC) was compared with the traditional PI controller. Simulation results show that the control strategy has better speed regulation performance and response speed when the motor is started with no load and subjected to external disturbances, while also possessing better robustness and anti-interference capability.

The detection of noise and current in the diagnosis of faults in automotive power seats cannot be performed simultaneously, and existing detection technologies cannot detect newly added functions of the seats. This paper designs and develops an automotive power seat fault detection system based on LabVIEW, which consists of hardware and software parts. It can synchronously collect noise and current signals generated during seat operation. The LabVIEW detection program processes the collected data in real-time, including waveform and numerical display, data analysis and evaluation, data storage, and retrieval functions. Finally, the system is tested and validated in actual production, demonstrating its efficient and accurate detection of faulty seats and its practical application value.

In the process of converter iron and steel smelting, the post-combustion oxygen lance can be used to achieve high efficiency post-combustion. The structure of oxygen lance nozzle directly affects the jet and secondary combustion behavior. In this paper, the 260 t converter is taken as the research object, and the three-dimensional model is established by using SolidWorks to explore the jet behavior of the secondary oxygen jet hole of the secondary combustion oxygen lance in the smelting process. Based on the analysis of the jet characteristics, the influence of the angle of the secondary oxygen nozzle on the jet behavior of the secondary combustion oxygen lance was discussed. The simulation results show that the angle of the auxiliary oxygen nozzle has an important influence on the offset and suction of the auxiliary oxygen nozzle jet, the jet velocity, the impact force, and the contact area with CO and then directly affects the efficiency of CO secondary combustion. These also provide a theoretical support for that optimization design of the auxiliary oxygen nozzle hole of the secondary combustion oxygen lance.

This article proposes a model for rolling bearing fault diagnosis based on variational mode decomposition combined with a convolutional neural network and a bidirectional long short-term memory network fusion (VMD-CNN-BiLSTM). In order to better extract the features of the original vibration signal, the Osprey Cauchy Sparrow search algorithm (OCSSA) was used to optimize the VMD parameters. Then, the optimal IMF component was extracted and calculated to construct the required feature vectors for the model. The feature vectors are used as inputs to train the CNN BiLSTM model. Finally, the model can accurately identify the fault status of rolling bearings. Experimental results have shown that the diagnostic model based on VMD-CNN-BiLSTM not only achieves a significant reduction in training time but also has an accuracy of 99.33%.

The structural design of molten salt storage tanks is of paramount importance in solar thermal power plants as it directly impacts their safe, stable, and efficient continuous power generation. This study is based on an engineering project within a solar thermal power plant located in Qinghai Province, China. It conducts a comprehensive analysis and research on high-temperature molten salt storage tanks. Using finite element software ABAQUS, shell element models and axisymmetric solid element models are established to conduct thermal coupling analysis on various aspects: the stability of the grid shell structure atop the storage tank, the integrity of the shell-to-bottom weld within the tank structure, as well as the tank wall plate and tank bottom plate. The research findings indicate that stress on the tank wall exhibits a characteristic pattern of oscillatory decrease from bottom to top, with stress concentration mainly occurring at the shell-to-bottom weld. Stress assessment conducted in accordance with ASME regulations confirms compliance with requirements. Moreover, it is observed that the friction coefficient does not directly impact the static stress of the storage tank. Instead, its significance lies in its effect on the expansion caused by high temperature, thus influencing the tank structure. Under thermal coupling effects, the friction coefficient significantly alters the stress distribution of the first-layer wall panel and the shell-to-bottom weld area. As the friction coefficient increases, the constraint effect of the foundation on the transverse expansion deformation of the bottom plate intensifies, leading to detachment of the shell-to-bottom weld from the foundation’s top surface, with detachment height continuously increasing.

A low-voltage distributed photovoltaic flexible control technology is developed based on the basic principle of minimizing solar energy loss and maximizing the output of photovoltaic power sources. It achieves flexible management of distributed power supply voltage based on the voltage deviation method at the grid-connected point of distributed photovoltaics. Combined with the three operational states of the distribution network—normal, warning, and emergency—it designs flexible control strategies and control processes for distributed power sources that are suitable for different grid operating states. At the same time, field pilot verification was conducted in collaboration with distributed photovoltaic pilot users. The verification results show that the impact of distributed photovoltaic grid connection on the power grid has been alleviated after flexible control, and voltage fluctuations, frequency, daily average power, and daily power generation have also been significantly improved.

As a crucial industrial AC equipment, the motor is subject to rigorous safety and stability standards. However, due to the motor’s closed operation, it is challenging to detect and diagnose internal faults directly. This is typically achieved by monitoring the motor’s input and output current signals and developing algorithms to process these signals. This indirect approach allows for the identification of potential internal motor issues. In this paper, we present a series of simulation experiments designed to simulate the normal and faulty operation states of a three-phase asynchronous motor. The experiments employ the collection of three-phase current waveforms under different operational conditions, frequency domain analysis via Fourier transform, and the extraction and analysis of current harmonic components. The selection of the second and third harmonic components for the main characteristics of the current harmonic components serves as the basis for the judgment of detecting motor faults. This provides a theoretical basis for the actual harmonics detection and judgment of the fault design. This provides a theoretical basis.

To meet the needs of large-capacity and high-quality development of wind turbines, further improve the stability and dynamic response performance of wind power output, and reduce the dynamic load of key components, a large-scale wind turbine adaptive PI-independent pitch control strategy considering load reduction is proposed. Based on the ROSCO controller, independent pitch control is implemented for the three blades of the wind turbine. Taking the 5 MW wind turbine of the National Renewable Energy Laboratory as an example, a model is built on the MATLAB simulation platform for simulation analysis and verification. The results show that compared to the unified pitch control strategy of the ROSCO controller, the proposed method can effectively suppress output power fluctuations, reduce tower loads of wind turbines, and have better adaptability to wind speed changes and fast response ability. Its advantages are more prominent in harsh wind conditions, such as turbulent wind.

The trend toward the electrification of aerial work platforms is becoming increasingly evident. With the application of driving motors on them, it is unreasonable to continue relying solely on tire friction for steering. Therefore, this manuscript introduces the Ackermann and Jeantand principle, which is widely used in electric vehicles, to establish a simulation model. This model simulates the laws of wheel speed and the required output speed of the motor under different steering angles of the aerial work platform. It is found that the speed of the right front wheel changes the most, increasing with the increase of the steering angle.

To effectively reduce the noise generated by motor vibrations, the relationship between rotor step with skewing and motor electromagnetic noise is investigated. Based on the ANSYS simulation platform, the radial electromagnetic force and noise of an 8-pole, 48-slot permanent magnet synchronous motor are simulated and analyzed under different numbers of segments. Using Fourier decomposition, the spatiotemporal distribution of the main orders of radial electromagnetic force before and after skewed segmentation, as well as the changes in electromagnetic noise under different segmentation conditions, is obtained. The impact of rotor step with skewing on motor vibration noise is analyzed. The simulation results show that when the motor adopts rotor step with skewing, there is a significant improvement in the radial electromagnetic force and noise generated during vibration compared to a motor without rotor segmentation. Furthermore, as the steps increase, the suppressive effect of the skewed segments on motor vibration noise becomes more pronounced.

As an important functional component of precision machine tools, improving the performance of static pressure rotary bearings is crucial for the development of high-end CNC machine tools. For the optimization problem of static pressure bearings with multidimensional nonlinear problems, based on multi-oil pad static pressure thrust bearings, with oil film stiffness, maximum tilting torque, and temperature rise as objective functions, and combined with a multi-objective particle swarm optimization parameter improvement optimization algorithm, a multi-objective optimization mathematical model was established to optimize parameter design. Without increasing the temperature, the oil film stiffness and tilting bearing capacity were improved. Finally, by manually dividing high-quality structured grids and establishing a model for flow field simulation analysis, it was found that the optimized design increased the oil film stiffness close to theoretical calculations, verifying the effectiveness of the optimization method. Provide a reference for the multi-objective optimization design of the oil chamber structure of hydrostatic bearings.

To investigate the thermal runaway propagation characteristics of lithium-ion power batteries, a two-dimensional heat dissipation model for a battery module incorporating phase change material (PCM) and an aluminum plate fin structure was developed. Employing ANSYS software, the study analyzed the thermal propagation characteristics of the battery module, considering scenarios where a pair of batteries in the middle of the module was induced to thermal runaway. The simulation results revealed that when the spacing between heat source batteries is narrow, the heat transfer is rapid and mutually influential, intensifying the propagation of thermal runaway and posing a significant threat to the safety of the battery system. However, when the spacing is wider, the intensity of thermal runaway propagation decreases, resulting in a relatively higher level of safety for the battery system.

In order to solve the problem of three-phase LLCL grid-connected inverter with harmonics at the switching frequency and at the resonance peak of the system under the weak grid, firstly, the comparative analysis of LLCL-type and LCL-type filters as well as the mathematical models when active damping method and passive damping method are respectively used, and then a hybrid damping method combining the active damping method and the passive damping method is proposed, i.e., based on the active damping method, a small resistor is added in series in the capacitive-inductive branch, combining the advantages of the two, to reduce the problem of long control delay of the active damping method without increasing the loss. The MATLAB simulation results show that the hybrid control strategy has a better harmonic suppression capability compared with the active damping method.

In the rapidly evolving field of electric vehicle (EV) technology, enhancing the performance of electric motors, specifically the widely used three-phase Permanent Magnet Synchronous Motor (PMSM), is a key area of research. The focus is on improving efficiency and effectiveness through advanced control strategies. This study begins by examining the electric drive system’s design and then develops a mathematical model for the PMSM, applying the zero direct-axis current ( $$id = 0$$ i d = 0 ) vector control approach for regulation. Simulations in MATLAB/Simulink were conducted to compare three modulation techniques: the seven-segment, five-segment, and Sine Pulse Width Modulation (SPWM) of Space Vector Pulse Width Modulation (SVPWM), over different operating frequencies. The results show that at a lower frequency of 60 Hz, the seven-segment SVPWM has lower Total Harmonic Distortion (THD) compared to the other methods, while the five-segment approach exhibits significant torque ripples due to its use of zero vectors. At a higher frequency of 90 Hz, the THD for all techniques increases, leading to efficiency losses. However, the five-segment SVPWM demonstrates superior efficiency at these frequencies due to lower switch energy consumption. In conclusion, the seven-segment SVPWM is preferable for stable EV operation at lower frequencies, and the five-segment SVPWM is more efficient at higher frequencies. The findings provide a solid theoretical foundation and practical guidance for the advancement of PMSM modulation strategies in the field of electric vehicles.