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The three-volume set CCIS 761, CCIS 762, and CCIS 763 constitutes the thoroughly refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2017, and of the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, held in Nanjing, China, in September 2017. The 208 revised full papers presented were carefully reviewed and selected from over 625 submissions.
The papers of this volume are organized in topical sections on: Biomedical Signal Processing; Computational Methods in Organism Modeling; Medical Apparatus and Clinical Applications; Bionics Control Methods, Algorithms and Apparatus; Modeling and Simulation of Life Systems; Data Driven Analysis; Image and Video Processing; Advanced Fuzzy and Neural Network Theory and Algorithms; Advanced Evolutionary Methods and Applications; Advanced Machine Learning Methods and Applications; Intelligent Modeling, Monitoring, and Control of Complex Nonlinear Systems; Advanced Methods for Networked Systems; Control and Analysis of Transportation Systems; Advanced Sliding Mode Control and Applications; Advanced Analysis of New Materials and Devices; Computational Intelligence in Utilization of Clean and Renewable Energy Resources; Intelligent Methods for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Computing and Control in Power Systems; Modeling, Simulation and Control in Smart Grid and Microgrid; Optimization Methods; Computational Methods for Sustainable Environment.



Computational Intelligence in Utilization of Clean and Renewable Energy Resources


Research on Wind Speed Vertical Extrapolation Based on Extreme Learning Machine

In engineering, the method of wind speed vertical extrapolation is based on the actual wind data of the wind tower, and the wind shear index is used to calculate the wind speed at any height in the near ground. The wind shear index is only considered in the neutral state of the atmosphere, without considering the impact of atmospheric stability on the wind shear index, which has some limitations. At the same time, the calculation of the wind shear index is a rather complicated task when considering the atmospheric stability. In order to solve these problems, this paper puts forward to use extreme learning machine for fitting the relationship between wind speed at different heights. Extreme learning machine has the advantages of fast learning speed, good generalization ability and so on. In this paper, the results obtained by the extreme learning machine and traditional methods are compared with the measured values. The results show that the extreme learning machine has a better application prospect in the vertical wind speed extrapolation.

Hui Lv, Guochu Chen

Optimal Scheduling of Wind Turbine Generator Units Based on the Amount of Damage of Impeller

Impeller (blade and wheel) is one of the key components of wind turbine. According to different degrees of leaf and root damage and hub damage amount, a multi-objective scheduling model of wind farm with wind turbine impeller damage, wind turbine startup rate, and the uncertainty of the output of generator is established to improve the model output allocation strategy. Then optimize the model with the adaptive discrete particle swarm (ADPSO) and artificial bee colony algorithm (ABC), then obtaining the target power value and start-stop group. In combination with the practical example, the simulation results show that the proposed method optimizes the start-up and shut-down times of the wind turbines and improves the operating life of the wind turbines.

Kai Lin, Guochu Chen

A Short Term Wind Speed Forecasting Method Using Signal Decomposition and Extreme Learning Machine

In this study, a novel hybrid model using signal decomposition technique and extreme learning machine (ELM) is developed for wind speed forecasting. In the proposed model, signal decomposition technique, namely wavelet packet decomposition (WPD), is utilized to decompose the raw non-stationary wind speed data into relatively stable sub-series; then, ELMs are employed to predict wind speed using these stable sub-series, eventually, the final wind speed forecasting results are calculated through combination of each sub-subseries prediction. To evaluate the forecasting performance, real historical wind speed data from a wind farm in China are employed to make short term wind speed forecasting. Compared with other forecasting method mentioned in the paper, the proposed hybrid model WPD-ELM can improve the wind speed forecasting accuracy.

Sizhou Sun, Jingqi Fu, Feng Zhu

A Novel Method for Short-Term Wind Speed Forecasting Based on UPQPSO-LSSVM

In order to improve the accuracy of the short-term wind speed forecasting, this paper presents a novel wind speed forecasting model based on least square support vector machine (LSSVM) optimized by an improved Quantum-behaved Particle Swarm Optimization algorithm called up-weighted-QPSO (UPQPSO), which uses a non-linearly decreasing weight parameter to render the importance of particles in population in order to have a better balance between the global and local searching. The developed method is examined by a set of wind speeds measured at mean half an hour of two windmill farms located in Shandong province and Hebei province, simulation results indicate UPQPSO-LSSVM model yields better predictions compared with QPSO-LSSVM and ARIMA model both in prediction accuracy and computing speed.

Wangxue Nie, Jingqi Fu, Sizhou Sun

Structure Design and Parameter Computation of a Seawater Desalination System with Vertical Axis Wind Turbine

This paper proposes a method to firstly convert wind energy into thermal energy by a vertical axis wind turbine and then use thermal energy to evaporate seawater for fresh water generation. The working principle and structure characteristics of the S type vertical axis wind turbine,liquid-stirring heater and seawater evaporation chamber are described. Mathematical calculation of bucket diameter and other parameters of the liquid-stirring heater are carried out according to the driving torque of wind turbine. Evaporating chamber capacity for seawater desalination is determined according to the liquid-stirring heater. These calculation models are introduced including power, torque, tip speed ratio, height and diameter of wind turbine; power, torque, rotating speed, blades diameter and other structural parameters of liquid-stirring heater; diameter, height of evaporator chamber. Generated fresh water from the seawater desalination system under rated wind speed is estimated at 239.1 g/h, which verify the feasibility of this kind of wind-powered seawater desalination method.

Yihuai Hu, Kai Li, Hao Jin

Inertial Response Control Strategy of Wind Turbine Based on Variable Universe Fuzzy Control

Wind turbines connect to the power grid through power converters, which makes the lack of effective synchronization relationship between generator speed and grid frequency. Existing strategies usually add an additional controller to utilize the hidden kinetic energy in wind turbines for frequency modulation, but this controller heavily dependents on the droop coefficient and the inertia constant. To avoid the influence of the two factors on the frequency modulation, this paper proposes an adaptive fuzzy control strategy for the inertial response of wind turbine. Its universe can be changed with frequency deviation and rate of change of frequency (ROCOF). By comparison of the synthesize inertia control and common fuzzy control, results of our adaptive fuzzy controller show the advantages of accuracy and applicability.

Le Gao, Guoxing Yu, Lan Liu, Huihui Song

System Frequency Control of Variable Speed Wind Turbines with Variable Controller Parameters

System operators are require wind power plants to provide system frequency control to secure safe operation of power systems. This paper first discussed the available amount of kinetic energy from wind turbines which could be released to provide extra power, minimum rotor speed of wind turbines operate at different condition to provide system frequency control is defined. The strategy to determine the wind turbine frequency controller parameter values is proposed, which will release all the available kinetic energy to provide system frequency support, this strategy also make sure the wind turbine rotor speed drop during frequency support is within the limited range, which ensure the stable operation of the turbine. The proposed strategy is tested by simulations carried out with Matlab/Simulink, which demonstrated the improvements on wind turbine operation and system frequency control effect.

Guoyi Xu, Chen Zhu, Libin Yang, Chunlai Li, Jun Yang, Tianshu Bi

Base-Load Cycling Capacity Adequacy Evaluation in Power Systems with Wind Power

Large scale penetration of intermittent wind power may result in base-load cycling capacity (BLCC) shortage problem, which poses an adverse impact on secure operation of power systems. The integration scale of wind power is heavily relevant to the BLCC adequacy. Therefore, it is important to evaluate the BLCC adequacy of power systems. Using probabilistic production simulation technology, a BLCC adequacy evaluation method considering the forced outage of conventional generation units is developed in this paper. In this method, several BLCC adequacy indexes are defined, namely the probability of BLCC shortage index, the expectation of BLCC shortage index, and the expectation of BLCC margin index. A scenario reduction technique is employed to tackle the uncertainty of wind speed. Numerical examples are presented to verify the reasonableness and effectiveness of the proposed method. This work is helpful to determine the appropriate wind power integration scale in power systems.

Jingjie Ma, Shaohua Zhang, Liuhui Wang

MFAC-PID Control for Variable-Speed Constant Frequency Wind Turbine

Due to the randomness and fluctuation characteristics of wind power, those model-based systems having intrinsically nonlinear are harder to be controlled. Based on the variable-speed constant frequency wind power generator, this paper presents a MFAC-PID control strategy to realize model-free, I/O data based dynamic control. Firstly, a control input criterion is established for optimal design, which realizes the targets of maximum wind energy capture and smoothing power point tracking. Then, by the usage of model free adaptive control (MFAC), a series of equivalent local linearization models are built using time-varying pseudo-partial derivative (PPD), which could be estimated only by I/O measurement data. Finally, considering that both MFAC and PID will generate incremental output, a constrained MFAC-PID algorithm is proposed in order to obtain the optimal input. The proposed strategy is verified with comparison to PID and MFAC methods. Results prove that MFAC-PID algorithm guarantees the convergence of tracking error at full wind speed.

Qingye Meng, Shuangxin Wang, Jianhua Zhang, Tingting Guo

A Multivariate Wind Power Fitting Model Based on Cluster Wavelet Neural Network

In this paper, we select the hierarchical cluster method to classify the wind energy level with the meteorological data, and then apply the 0–1 output method to quantify the wind energy level. Next, we utilize wavelet neural network to fit multivariate wind power data, which solves the problem of randomness, intermittency and volatility of wind power data. Finally, a wind-power numerical experiment shows the ideal fitting results with an error precision of $$1.71\%$$ and demonstrates the effectiveness of our model.

Ruiwen Zheng, Qing Fang, Zhiyuan Liu, Binghong Li, Xiao-Yu Zhang

Control Strategy for Isolated Wind-Solar-Diesel Micro Grid System Considering Constant Load

With the technologies of renewable energy maturing, the micro grid will become more competitive. Based on constant load, the paper builds an experiment of the isolated wind-solar-diesel micro grid system. Furthermore, the experiment achieves the goal that it can rationally regulate the diesel generators group, allocate the output power of the renewable energy according to the changes of the weather conditions and the power scheduling strategy. The feasibility and the effectiveness of the proposed approach are proved by the result of an isolated micro-grid experiment.

Xuejian Yang, Dong Yue, Tengfei Zhang

Equilibrium Analysis of Electricity Market with Wind Power Bidding and Demand Response Bidding

In electricity markets with strategic bidding of wind power, it is important to handle wind power’s output deviation. In this paper, the scenario where customers in Demand Response (DR) program matches the wind power’s output deviation through strategic bidding is studied, and a stochastic equilibrium model of the electricity market with wind power bidding and demand response bidding is proposed. In this model, linear supply function bidding is applied by both wind power producers and traditional power producers to match power demand in wholesale market. In order to compensate for the wind power’s output deviation, two market models in balancing market for demand response are proposed where supply function bidding and demand function bidding are applied by DR customers to match supply deficit and surplus respectively. Furthermore, the penalty cost for output deviation of the wind power producer is determined by the equilibrium price in balancing market. The equilibrium problems are solved by being reverse-engineered into convex optimization problems and the existence and uniqueness of the Nash equilibrium is theoretically proved. A distributed dual gradient algorithm is further proposed to achieve the equilibrium. Numerical examples are presented to verify the validity of the proposed model and effectiveness of the algorithms.

Kai Zhang, Xian Wang, Shaohua Zhang

Stability Analysis of Wind Turbines Combined with Rechargeable Batteries Based on Markov Jump Linear Systems

To maximize the output power in low wind speed and to maintain the demanded power of the turbine in high wind speed, switch control strategy is applied to wind turbines combined with rechargeable batteries. A mathematical model of a Markov jump linear system is established for such wind turbine systems. The method for determining the transition probability of the Markov chain is also presented. Then sufficient conditions for almost sure ability are proposed for this combined wind turbine system.

Xiao-kun Dai, Yang Song, Mira Schüller, Dieter Schramm

Modeling and Simulation Study of Photovoltaic DC Arc Faults

The DC arc fault is a major threat to the safety of photovoltaic systems, a large amount of heat from sustained arcs leads to fire accidents. Therefore, detecting the arc faults for PV systems is receiving considerable concern. In order to develop accurate and rapid detection and location methods for arc faults, it is important to establish an arc model to characterize and predict arc characteristics and transient response. In this paper, a new DC arc model is developed from a hyperbolic approximation by observing the arc current and voltage waveforms. Based on the derived model, pink noise is superimposed to obtain better frequency domain characteristics. After comparing the simulation and the experimental results, the model is proved to be suitable for transient simulations. Furthermore, developing detection algorithm and location strategies will also be based on the DC arc model.

Zhihua Li, Zhiqun Ye, Chunhua Wu, Wenxin Xu

Data Management of Water Flow Standard Device Based on LabVIEW

It is very important to strengthen the research on water flow standard device. The software LabVIEW is used as the development platform of the software control system for the flow standard device. A large amount of data is involved in the calibration process. A method of combination of the database system and file system is adopted to manage all data involved in the device on the basis of the actual needs. It can simplify data operations, process the calibration data automatically. In this way, a high accuracy for the calibration will be ensured and the automation level of the device will be improved.

Shaoshao Qin, Bin Li, Chao Cheng

Design and Research of Water Flow Standard Facilities Based on Field Service

A flow standard facility based on field service is designed to solve the problem of on-site high-precision parameter metering for water flow standard facilities, in which the water is take as the medium, the mass flowmeters are used as the transfer standard of liquid flow, the variable frequency pump and the surge tank are acted as the secondary regulation system, and the hardware and software platform of LabVIEW are conducted as the development system. The entire facility is small and light weight so that it can be carried to the scene for rapid calibration. Because it changes from sent by customers to sending calibration to customers and saves the standby time caused by disassembling and sending flowmeters, it improves the production efficiency of enterprises greatly. Ultimately, the facility can be produced and it will fill the domestic technical gap of water flow calibration based on field service.

Chao Cheng, Bin Li, Shaoshao Qin

An Improved Multi-objective Bare-Bones PSO for Optimal Design of Solar Dish Stirling Engine Systems

An improved bare-bones multi-objective particle swarm optimization, namely IMOBBPSO is proposed to optimize the solar-dish Stirling engine systems. A new simple strategy for updating particle’s velocity is developed based on the conventional bare-bones PSO, aiming to enhance the diversity of the solutions and accelerate the convergence rate. In order to test the effectiveness of IMOBBPSO, four benchmarks are used. Compared with the non-dominated sorting genetic algorithm-II (NSGAII) and multi-objective particle swarm optimization algorithm (MOPSO), it is revealed that IMOBBPSO can quickly converge to the true Pareto front and efficiently solve practical problems. IMOBBPSO is then used to solve the design of the solar-dish Stirling engine. It is shown that IMOBBPSO obtains the best optimization results than NSGAII and MOPSO. It further achieves significant improvements 25.6102% to 29.2926% in terms of the output power and entropy generation rate when it is compared with existing results in the literature.

Qun Niu, Ziyuan Sun, Dandan Hua

Fault Diagnosis Method of Ningxia Photovoltaic Inverter Based on Wavelet Neural Network

Accurate fault diagnosis is the premise to ensure the safe and reliable operation of photovoltaic three-level inverter. A fault diagnosis method based on wavelet neural network is researched in the paper. First of all, the topology and the fault characteristics of three-level inverter are analyzed, the fault features are analyzed for three-level inverter when single and double IGBTs fault, the eigenvectors of phase voltage, the upper bridge arm and the lower bridge arm voltage are extracted by three-layer Wavelet Package Transform, the BP neural network is designed for training data and testing. The simulation model is built by Matlab/Simulink, the simulation results show that the method can accurately diagnose for various fault circumstances.

Guohua Yang, Pengzhen Wang, Bingxuan Li, Bo Lei, Hao Tang, Rui Li

Research on Expert Knowledge Base of Intelligent Diagnosis Based on Tubing Leakage of High-Pressure Heater in Nuclear Power Plant

In order to improve the accuracy and timeliness of the tubing leakage of the high-pressure heater in the nuclear power plant, the fault diagnosis is carried out with the terminal difference triggered by the heat economy of the unit. Through the mechanism modeling of the tubing leakage of the high-pressure heater, the set of the symptom parameters related to the fault are obtained. The expert knowledge base of the tubing leakage in fault diagnosis system of the high-pressure heater is analyzed by using the mathematical statistics and the experience of the on-site experts. Through the insert of man-made fault in the 1000 MW nuclear power model, the intelligent diagnosis expert system is used to fault diagnosis. The results show that the method can accurately diagnose the tubing leakage of the high-pressure heater by analyzing the monitoring parameters at the beginning of the fault, and prove the validity and feasibility of the knowledge base.

Miao Zheng, Hong Qian, Siyun Lin, Bole Xiao, Xiaoping Chu

Research on Intelligent Early-Warning System of Main Pipeline in Nuclear Power Plants Based on Hierarchical and Multidimensional Fault Identification Method

In order to improve the timeliness and accuracy of the fault identification for SB-LOCA (small break-loss of coolant accident), a hierarchical and multidimensional fault identification method is proposed, and a intelligent early-warning system is established to locate and evaluate the degree of the fault in the early stage, which can improve the operating safety of nuclear power plants. The faults in different kinds of locations and degrees are artificially inserted into the nuclear power simulator and are recognized by the early-warning system based on the method researched above. The results show that it can accurately locate and evaluate the tiny degree of fault, which verifies the validity and feasibility of the intelligent early-warning system.

Hong Qian, Siyun Lin, Miao Zheng, Qiang Zhang

The Early Warning System of Nuclear Power Station Oriented to Human Reliability

In order to improve the reliability of nuclear power plant operators in the face of the abnormal operation of nuclear island, this paper studies the early-warning system of nuclear power plant through the abnormal operation parameters of nuclear island. In this paper, the object studied about is the fault of the passive equipment of the reactor. After the reaction shutdown, operators can take the emergency measures in an accurate and timely manner. The early warning system will show how the fault is expected in the operational measures, so that operators can do respond to prepare. The early warning system studied in this paper is applied to nuclear power simulation system. Through the research on abnormal operation simulated by nuclear power simulation system, the results show that the early-warning system can improve human reliability of nuclear power plant in the face of abnormal operation.

Shuai Ren, Hong Qian

Research on Energy Interconnection Oriented Big Data Sharing Platform Reference Architecture

In order to provide a unified data sharing service support for energy interconnection business, big data application development and operation, the large power data sharing platform for energy interconnection will integrate data storage, data calculation, data analysis and data service functions. This platform will not only invigorate the power data assets, bringing enormous economic benefits, but also promote economic restructuring and energy saving and emission reduction. This paper analyzes the development requirements of energy interconnection, and then designs the general framework, functional framework, technical framework and deployment framework of the power big data platform which is suitable for the energy interconnection. Finally, this paper lists the application flow and strategy of the big data platform, which is under the typical scenarios of transmission monitoring and status assessment real-time analysis and distribution network planning off-line analysis.

Wei Rao, Jing Jiang, Ming Yang, Wei Peng, Aihua Zhou

Intelligent Methods for Energy Saving and Pollution Reduction


Study on Lightweight Design and Connection of Dissimilar Metals of Titanium Alloy TC4/T2 Copper/304 Stainless Steel

Under the background of lightweight design, manufacturing and improvement of comprehensive performance, dissimilar metals connection has been becoming a research focus recently and will have a broad application prospect. Titanium alloy TC4 and 304 stainless steel have many excellent properties, achievement of effective connection between these two materials has a significant promoting effect on Industrial Technology. However, the bonding connection of TC4/304 is very poor, so it is necessary to redesign the joint and further study the strengthening mechanism. In the paper, connection experiment of TC4/304 was carried out using two methods: Electron Beam Welding and Friction Stir Welding. Optical microscopy, SEM, EDS were applied for the analysis of microstructure and phase structure. The results state that EBW and FSW are effective and the maximum strength are 196 Mpa and 178 Mpa respectively. Both failure mode are brittle fracture.

Shun Guo, Qi Zhou, Peng Xu, Qiong Gao, Tianyuan Luo, Yong Peng, Jian Kong, KeHong Wang, Jun Zhu

Research on Warehouse Scheduling Optimization Problem for Broiler Breeding

Feeding on time, which is a key factor for the healthy growth of broilers. To minimize the feeding delay, a mathematical model considering the time spent on transferring feed is proposed. To solve the model above, a fruit fly algorithm (FFA) is adopted. Considering its disadvantages of trapping into local optima and low convergence accuracy, mutation operator and adaptive step-length is imposed to form an improved fruit fly algorithm (IFFA), which not only enhanced the convergence efficiency, but also ensured the global optimization. Finally, to verify the performance of the proposed algorithm, it is compared with FFA and genetic algorithm (GA). Simulation results prove that the feasibility and superiority of the proposed algorithm.

Wenqiang Yang, Yongfeng Li

A Discrete Fourier Transform Based Compensation Task Sharing Method for Power Quality Improvement

In this paper, a discrete Fourier transform (DFT) based compensation task sharing method is proposed for the improvement of power quality of main grid. Power quality problem induced by typical nonlinear loads is tackled by the cooperation of multi-functional grid-tied inverters (MFGTIs) with the compensation instruction as part of its reference. Unlink the ordinary method where communication is avoided, Low-bandwidth channel is used to transmit the compensation reference after the current data are calculated by DFT. Simulation results are presented to demonstrate the effectiveness of the proposed method.

Jianbo Chen, Dong Yue, Chunxia Dou, Chongxin Huang

A Comprehensive Optimization of Controller Design for Trade-off of Energy and System Performance

This paper investigates the optimal trade-off between the system performance and control energy consumption for different settings of the control parameters $${k_P}, {k_D}, {\mu }$$ and $$P{D^{\mu }}$$ controller design, and a comprehensive optimization method is proposed to obtain the optimal $$P{D^{\mu }}$$ controller. Detailed correlation analysis between the control performance and energy consumption is presented. The method is applied to the control of a ball-beam system, and the simulation results confirm that the proposed method is practically useful in the analysis and design of the $$P{D^{\mu }}$$ controller.

Ke Zhang, Min Zheng, Kang Li, Yijie Zhang

Hierarchical Time Series Feature Extraction for Power Consumption Anomaly Detection

Anomaly of power consumption, particularly due to electricity stealing, has been one of the major concern in power system management for a long time, which may destroy the demand-supply balance and lead to power grid regulating issues and huge profit reduction of electricity companies. One of the essential key to develop machine learning model to solve the above problems is time series feature extraction, which may affect the superior limit of machine learning model. In this paper, a novel systematic time series feature extraction method named hierarchical time series feature extraction is proposed, used for supervised binary classification model that only using user registration information and daily power consumption data, to detect anomaly consumption user with an output of stealing probability. Performance on data of over 100,000 customers shows that the proposed methods are outperforming one of the existing state-of-the-art time series feature extraction library tsfresh [1].

Zhiyou Ouyang, Xiaokui Sun, Dong Yue

Prospect Theory Based Electricity Allocation for GenCos Considering Uncertainty of Emission Price

Under the electricity market environment, power generation companies (GenCos) can either sell electricity through the spot market or sell them through bilateral contracts. GenCos have to make electricity allocation strategies among different trading choices facing uncertainty of spot market prices. In addition, uncertainty of the emission price is increasing and will become an important risk factor for fossil fuel GenCos. In this paper, we develop a risk decision model for fossil fuel GenCos’ electricity allocation based on the prospect theory, which considers GenCos’ loss aversion characteristic. Under uncertainties of the electricity spot market price and emission price, the model maximizes the GenCo’s overall prospect value through allocating reasonably electricity between the spot market and bilateral contracts. The simulation results show that GenCos’ psychological expected profit and loss aversion characteristic have significant effects on their risk decision-making. As uncertainty of the emission price increases, fossil fuel GenCos will increase electricity sale in the spot market.

Yue Zhang, Shaohua Zhang

Neural-Network-Based Tracking Control of Offshore Steel Jacket Platforms

This paper deals with the problem of neural network tracking control for an offshore platform system under external wave forces. A feedforward backpropagation neural-network-based tracking controller (NNTC) is designed to attenuate the displacement response of the offshore platform. In the simulation, the proposed NNTC scheme can effectively improve the stability of the offshore platform. Furthermore, the designed NNTC is more robust than the feedforward and feedback optimal tracking controller (FFOTC) in terms of system parametric perturbations and external wave loads.

Zhi-Hui Cai, Bao-Lin Zhang, Xian-Hu Yu

Intelligent Methods in Developing Electric Vehicles, Engines and Equipment


Short-Term Optimal Scheduling with the Consideration of Electric Vehicle Driving Rules

Taking into account the daily driving rules of electric vehicle (EV), a novel short-term optimal scheduling model is proposed. And to describe the driving characteristics of the EV, EVs are divided into four types according to the detailed driving rules. And the stochastic driving time, access time and daily mileage of different types of EVs are simulated by a large number of scenes. Besides, the interaction between the EV and the power systems is added to establish the coupling between the output of units, and the operation of EVs. Due to the complexity of the constraints, the stochastic nonlinear unit commitment model is converted into mixed integer linear programming problem and solved by CPLEX. Case studies show the necessity of considering the stochastic driving rules of EVs, and the classification of EVs can make the dispatching decision more reasonable.

Xiaolin Ge, Chenhao Pei

Dispatching Analysis of Ordered Charging Considering the Randomness Factor of Electric Vehicles Charging

With the popularity of Electric Vehicles (EV), the access of electric vehicles makes the load curve of distribution network becomes more and more steep. The increase of EV load brought big impact on the power system and it is necessary to analyze random factors affecting the electric vehicle charging load. Distribution grid dispatching can reduce the gap between peak load and valley load so as to ensure power grid normal operation and power quality. Firstly EV charging load model is established based on the analysis the random factors affecting EV load, and the accumulation method of EV load is given. Secondly the dispatching model of coordination charging is built considering the randomness of EV charging, and the genetic algorithm was used to solve the model. Finally, with the IEEE 33 node test system, the load curve of power grid is obtained in the mode of disorderly charging and order charging dispatching, which proves the validity of the charging dispatching model, and the feasible strategies are provided by the analysis of simulation results.

Ling Mao, Enyu Jiang

A Contract Based Approach for Electric Vehicles Charging in Heterogeneous Networks

With the help of mobile charging stations (MCSs), the charging service of electric vehicles (EVs) can be provided more easily with higher payoff and lower consumption, compared with the fixed charging stations (FCSs). Although many traditional approaches have been used to decide the pricing plans for FCSs, it can not be efficient to design the optimal pricing strategy for MCSs. In this paper, we propose a contract-based scheme to solve the problem of supplying power service to EV users. Firstly, considering quality of service (QoS) and mobility of MCS in the heterogeneous networks, we study and develop the utility function based on the relationship for MCS and EV users. Then, the charging problem for EV users is formulated as an optimization problem through the contract theory. Thirdly, we present the iterative algorithm to achieve the optimal solution. Our simulation results show the effectiveness of the proposed strategy.

Huwei Chen, Zhou Su, Yilong Hui, Hui Hui, Dongfeng Fang

Review of the Four Ports Electromechanical Converter Used for Hybrid Electric Vehicle

Four Ports Electromechanical Converter (FPEC) is a device based on electromagnetic principle to realize speed distribution, torque distribution and electromechanical conversion. The performance of FPEC directly affects the dynamic properties and fuel economy of hybrid electric vehicle (HEV), which can achieve the functions of continuously variable speed, power compensation, brake energy feedback, starter and generator mode by constituting a complete series-parallel hybrid system combining with the internal combustion engine (ICE) and energy storage. This paper has discussed the FPEC used for the hybrid power system and focused on the analysis of magnetic coupling and electrical coupling, which lays the foundation for the further research and practical application of FPEC in the HEV.

Qiwei Xu, Jing Sun, Meng Zhao, Xiaobiao Jiang, Yunqi Mao, Shumei Cui

Research on Parameters Matching of Hybrid Electric Vehicle with Compound-Structure Induction Machine

This paper analyzes the parameters matching problem of hybrid electric vehicle with compound-structure induction machine. According to vehicle dynamics model, the dynamic coupling of compound-structure induction machine can be studied. After the analysis of optimization for degree of hybridization, the research of parameters matching can be carried out. Furthermore, the design requirements of hybrid electric vehicle based on compound-structure induction machine are analyzed. As a result of above analysis, the requirements of three aspects: peak power, continuous power and energy are obtained. Finally, aid from the simulation of Cruise, it is verified that the parameters matching of optimization for degree of hybridization can be feasible.

Qiwei Xu, Xiaobiao Jiang, Meng Zhao, Xiaoxiao Luo, Weidong Chen, Yunqi Mao, Shumei Cui

Location Model Research of Charging Station for Electric Vehicle Based on Users’ Benefit

To improve the electric vehicle charging infrastructure and accelerate the development of electric vehicles, the optimization of electric vehicle charging stations’ location and size are studied in this paper. The annual comprehensive cost of society, including user cost and charging station cost, is regarded as the objective function. Weight coefficients are added to the function for increasing the proportion of user cost. An optimized mathematical model for electric vehicle charging stations based on users’ benefit is established. The improved Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is adopted to solve this mathematical mode, and result which contain the optimal location and size of electric vehicle charging stations is obtained. Finally, an actual area is taken as the case study to optimize the location and size of electric vehicle charging stations by solving the mathematical model proposed in this paper with the improved QPSO algorithm. Rationality and validity of the model are well improved by the scientific reasonable result.

Fei Xia, Zhicheng Wang, Daogang Peng, Zihao Li, Zhijiang Luo, Bo Yuan

Research on Double Fuzzy Control Strategy for Parallel Hybrid Electric Bus

In this paper, a double fuzzy control strategy (DFLS) for parallel hybrid electric bus (HEB) is proposed. Firstly, the basic parameters of HEB is designed. Then, the single fuzzy logic control strategy (SFLS) is proposed based on the parameters. SOC and torque demand scale factor are taken as the input of fuzzy controller. Combining with the braking energy recovery strategy, this paper proposes a double fuzzy control strategy, which is taken SOC, required torque and bus speed as the input. And this paper makes a comparison of the both strategies in Chinese Bus Driving Cycle (CBDC) based on the simulation results.

Qiwei Xu, Xiaoxiao Luo, Xiaobiao Jiang, Meng Zhao

Optimal Battery Charging Strategy Based on Complex System Optimization

This paper proposes a complex system optimization method to obtain an optimal battery charging strategy. First, a real-world lithium-ion battery charging model is built as a complex system problem, which includes electric subsystem and thermal subsystem. The optimization objectives of electric subsystem includes battery charging time and energy loss, and the optimization objectives of thermal subsystem includes battery internal temperature rise and surface temperature rise. Then a called biogeography-based complex system optimization (BBO/Complex) algorithm is introduced, which is a heuristic method for complex system optimization. Finally, BBO/Complex is applied to the complex system of battery charging strategy, and the results show that the proposed method is a competitive algorithm for solving batter charging problem studied in this paper.

Haiping Ma, Pengcheng You, Kailong Liu, Zhile Yang, Minrui Fei

Experimental Research on Power Battery Fast Charging Performance

Lithium-ion battery fast charging issues have become a crucial factor for the promotion of consumer interest in commercialization, such as mobile devices and electric vehicles (EVs). This paper focuses on the experimental research on fast charging. A battery thermal model is introduced to investigate the temperature variation at high charging current rates 1C, 3C, 4C, 5C. And charging experiments are taken at these current rates respectively. The results show that high charging current rates could effectively reduce charging time. Besides, batteries can be charged to 77.5%, 76.2%, 72.5% of the capacity at 3C, 4C, 5C current rates respectively. The maximum temperature rises during charging are 4.5 °C, 5.5 °C, 6.6 °C respectively.

Jinlei Sun, Lei Li, Fei Yang, Qiang Li, Chao Wu

A Novel RBF Neural Model for Single Flow Zinc Nickel Batteries

As a popular type of Redox Flow Batteries (RFBs), single flow Zinc Nickel Battery (ZNB) was proposed in the last decade without requiring an expensive and complex ionic membrane in the battery. In this paper, a Radial Basis Function (RBF) neural model is proposed for modelling the behaviours of ZNBs. Both the linear and non-linear parameters in the model are tuned through a new feedback-learning phase assisted Teaching-Learning-Based Optimization (TLBO) method. Besides, the fast recursive algorithm (FRA) is applied to select the proper inputs and network structure to reduce the modelling error and computational efforts. The experimental results confirm that the proposed methods are capable of producing ZNB models with desirable performance over both training and test data.

Xiang Li, Kang Li, Zhile Yang, Chikong Wong

State-of-Charge Estimation of Lithium Batteries Using Compact RBF Networks and AUKF

A novel framework for the state-of-charge (SOC) estimation of lithium batteries is proposed in this paper based on an adaptive unscented Kalman filters (AUKF) and radial basis function (RBF) neural networks. Firstly, a compact off-line RBF network model is built using a two-stage input selection strategy and the differential evolution optimization (TSS_DE_RBF) to represent the dynamic characteristics of batteries. Here, in the modeling process, the redundant hidden neurons are removed using a fast two-stage selection algorithm to further reduce the model complexity, leading a more compact model in line with the principle of parsimony. Meanwhile, the nonlinear parameters in the radial basis function are optimized through the differential evolution (DE) method simultaneously. The method is implemented on a lithium battery to capture the nonlinear behaviours through the readily measurable input signals. Furthermore, the SOC is estimated online using the AUKF along with an adaptable process noise covariance matrix based the developed RBF neural model. Experimental results manifest the accurate estimation abilities and confirm the effectiveness of the proposed approach.

Li Zhang, Kang Li, Dajun Du, Minrui Fei, Xiang Li

Intelligent Computing and Control in Power Systems


Design of Adaptive Predictive Controller for Superheated Steam Temperature Control in Thermal Power Plant

In this paper, an adaptive model predictive controller for overheating steam temperature control of thermal power plants is designed, which is based on the control object with large delay, large inertia, nonlinearity and strong time-varying properties. Through the on-line identification and control of different models, compared with predictive controllers in a general model, in terms of adjusting the superheat steam temperature, it can shorten adjusting time drastically, reduce even eliminate the overshoot and improve the dynamic performance greatly when applying in adaptive model predictive controller. The results show that the adaptive model predictive controller, because of its simple implementation, can be used in power plants, and also can be applied to solve similar problems, which has a broad application prospects.

Hong Qian, Yu-qing Feng, Zi-bin Zheng

Extended State Space Predictive Control of Gas Turbine System in Combined Cycle Power Plant

In this paper, an extended state space predictive control (ESSPC) strategy has been applied to gas turbine in combined cycle power plant. This proposed predictive control needn’t solve Diophantine equation online. In addition, in order to overcome the shortcoming of the conventional state space predictive control (SSPC) which only takes output errors into consideration, the objective function of ESSPC includes both output errors and the variation of the system states. In the rolling optimization part, the quadratic program (QP) method is applied to deal with the limitations on the inputs of system. Simulation results show that the proposed algorithm has better tracking ability and stability compared with conventional state space predictive controller in the same condition.

Guolian Hou, Tian Wang, Huan Du, Jianhua Zhang, Xiaobin Zheng

Decentralized H Load Frequency Control for Multi-area Power Systems with Communication Uncertainties

This paper investigates the distributed load frequency control (LFC) for multi-area power systems with communication switching topologies and data transmission time-delays. For stabilizing the power flow frequency while encompassing situations of seldom subsystem disconnections, a decentralized Markov switching control scheme is proposed. To further reduce conservative of the controller, a time-delay equipartition technique is developed. In addition, the distributed cooperative control (DCC) scheme is also discussed and proved to be unsuitable as a LFC strategy. Finally, illustrative examples are provided to validate effectiveness of the proposed methods.

Yanliang Cui, Guangtian Shi, Lanlan Xu, Xiaoan Zhang, Xue Li

Cyber Security Against Denial of Service of Attacks on Load Frequency Control of Multi-area Power Systems

While open communication infrastructures are embedded into multi-area power systems to support data transmittion, it make communication channels vulnerable to cyber attacks, reliability of power systems is affected. This paper studies the load frequency control (LFC) of multi-area power systems under DoS attacks. The state space model of power systems under DoS attacks is formulated, where event-triggered control scheme is integrated for the multi-area power systems under DoS attacks. By utilizing average dwell time design approach, exponential stability and $$L_2$$-gain of the multi-area power systems can be obtained for event-triggered LFC scheme under DoS attacks, if choosing an unavailability rate of communication channels for DoS attacks properly. Finally, the example shows that the convergences of frequency deviation of three-area power systems are compared under different DoS attack scenarios, when the proportion of the total time of DoS attacks can obtain the result properly.

Yubin Shen, Minrui Fei, Dajun Du, Wenjun Zhang, Srdjan Stanković, Aleksandar Rakić

Detecting Replay Attacks in Power Systems: A Data-Driven Approach

Detecting replay attacks in power systems is quite challenging, since the attackers can mimic normal power states and do not make direct damages to the system. Existing works are mostly model-based, which may either suffer from a low detection performance or induce negative side effects to power control. In this paper, we explore purely data-driven approach for good detection performance without side effects. Our basic idea is to learn a classifier using a set of labelled data (i.e., power state) samples to detect the replayed states from normal ones. We choose the Support Vector Machine (SVM) as our classifier, and a self-correlation coefficient as the data feature for detection. We evaluate and confirm the effectiveness of our approach on IEEE bus systems.

Mingliang Ma, Peng Zhou, Dajun Du, Chen Peng, Minrui Fei, Hanan Mubarak AlBuflasa

A Novel Dynamic State Estimation Algorithm in Power Systems Under Denial of Service Attacks

The paper is concerned with a dynamic state estimation algorithm in power systems under denial of service (DoS) attacks. Firstly, the character of data packet losses caused by DoS attacks is described by Bernoulli distribution, and the dynamic model of power system is reconstructed. Using Holt’s two-parameter exponential smoothing and extended Kalman filtering techniques, a dynamic state estimation algorithm is proposed, where the recursion formula of the parameter identification, state prediction and state filtering contain the statistical properties of data packet losses. Simulation results confirm the feasibility and effectiveness of the proposed algorithm.

Mengzhuo Yang, Xue Li, Dajun Du

Small-Signal Refinement of Power System Static Load Modelling Techniques

Loads are often represented as a weighted combination of constant impedance (Z), current (I) and power (P) components, so called ZIP models, by various power systems network simulation tools. However, with the growing need to model nonlinear load types, such as LED lighting, ZIP models are increasingly rendered inadequate in fully representing the voltage dependency of power consumption traits. In this paper we propose the use of small-signal ZIP models, derived from a neural network model of appliance level consumption profiles, to enable better characterizations of voltage dependent load behavior. Direct and indirect approaches to small-signal ZIP model parameter estimation are presented, with the latter method shown to be the most robust to neural network approximation errors. The proposed methodology is demonstrated using both simulation and experimentally collected load data.

Gareth McLorn, Seán McLoone

H ∞ Prediction Triggering Control of Multi-area Power Systems Load Frequency Control Under DoS Attacks

This paper is concerned with load frequency control of multi-area power system under DoS attacks. We introduce inner virtual event triggering mechanism under framework of model based predictive control to design load frequency control. Then Lyapunov stability theory is used to analysis $$ H_{\infty } $$ predictive control problem. Sufficient condition is derived in form of linear matrix inequalities which guarantees the closed loop system is stable with $$ H_{\infty } $$ performance. Simulation of a two-area power system is given to illustrate the effectiveness of the proposed method in dealing with long duration DoS attack.

Zihao Cheng, Dong Yue, Xinli Lan, Chongxin Huang, Songlin Hu

New Framework Mining Algorithm Based Main Operation Parameters Optimization in Power Plant

Association rule mining algorithm based on support-confidence framework is widely applied to the optimization of main operating parameters value in thermal power plant. But some important potential knowledge is easy to be overlooked by the framework in the actual mining process. Moreover, the simulation experiments show that there is a great relationship between mining results and a given minimum support threshold. Thus a dynamic interestingness-support framework mining algorithm based on metarules guided is proposed by which parameters for multidimensional association rules can be determined. The new framework reduces the redundancy of results by metarule-guided mining. And it mainly screens association rules with the index of interestingness except support, so as to weaken the dependence between mining results and the minimum support threshold. What is more, a new similarity criterion is introduced in dividing production process condition, to avoid the single spherical cluster determined by the Euclidean distance. Therefrom overcome the shortness of traditional dividing. The simulation results show that the algorithm proposed in this paper can effectively tap out the rules. And the rules can correctly reflect the knowledge of the unit and improve the accuracy of main operation parameters value in thermal power plant.

Wencheng Huang, Li Jia, Daogang Peng

A Consensus-Based Distributed Primal-Dual Perturbed Subgradient Algorithm for DC OPF

In this paper, an consensus-based distributed primal-dual perturbed subgradient algorithm is proposed for the DC Optimal Power Flow (OPF) problem. The algorithm is based on a double layer multi-agent structure, in which each generator bus and load bus in electric power grid is viewed as bus agent and connects with the grid by a network agent. In particular, network agents employ the average consensus method to estimate the global variables which are necessary for bus agents to update their generation using a local primal-dual perturbed subgradient method. The proposed approach is fully distributed and realizes the privacy protection. The employment of primal-dual perturbation method ensuring the convergence of the algorithm. Simulation results demonstrate the effectiveness of the proposed distributed algorithm.

Zhongyuan Yang, Bin Zou, Junmeng Zhang

Model Predictive Control Based on the Dynamic PLS Approach to Waste Heat Recovery System

This paper investigates model predictive control scheme based on PLS latent space for CO2 transcritical power cycle based waste heat recovery system. First, a control-oriented model is developed for the transcritical CO2 power cycle system. For the sake of solving multi-variable and strong coupling problems of the transcritical CO2 cycle system, model predictive control scheme based on the dynamic PLS approach is adopted and applied to this waste heat recovery system. The experimental results show that the adopted control method shows better performance in disturbance rejection and set-point tracking than PLS-PID control scheme for the CO2 transcritical power cycle system.

Jianhua Zhang, Haopeng Hu, Jinzhu Pu, Guolian Hou

Optimized Control of Ship DC Electric Propulsion System with Energy Storage Unit

The frequent load fluctuations caused by the marine environmental variability and the operational requirements of the ship itself will have adverse impacts on the economics and reliability of the ship power grid. To alleviate these adverse impacts, the energy management technology is adopted and the super capacitor is employed as the energy storage unit in the ship DC electric propulsion system. In addition, the smooth fluctuation power control method is used, and the particle swarm optimization algorithm is applied to optimize the cut-off frequency of the low-pass filter and the capacity of super capacitor. As results, the fuel consumption cost of the diesel generator and energy storage cost can be minimized, and the negative impact caused by the ship load fluctuations can be mitigated. Finally, the simulation results show that the proposed methods can effectively improve the performance of ship propulsion system.

Feng Ding, Shuofeng Wang, Shaohua Zhang

The Application of the Particle Swarm Algorithm to Optimize PID Controller in the Automatic Voltage Regulation System

Automatic voltage regulation (AVR) is a system that used to adjust the voltage stability and balance reactive power and also for regulating power plant generator. Focusing on the traditional PID automatic voltage regulation system, this paper investigated the effect of particle swarm optimization (PSO) algorithm in optimizing the parameters of PID controller in AVR system, and compared with genetic algorithm (GA) for PID parameters optimization. The simulation results showed that the AVR system optimized by PSO had more stability and robustness, which indicated the good application prospect of the proposed method.

Jing Wang, Naichao Song, Enyu Jiang, Da Xu, Weihua Deng, Ling Mao

Research on the Bio-electromagnetic Compatibility of Artificial Anal Sphincter Based on Transcutaneous Energy Transfer

For the treatment of anal incontinence, a new type of artificial anal sphincter is designed. The artificial anal sphincter system based on transcutaneous energy transfer mainly consists of sensor execution subsystem, wireless communication control subsystem and transcutaneous energy supply subsystem. Aim at the energy supply problem and the electromagnetic compatibility of the device, the energy transmission circuit is designed and optimized. At the same time, the three-dimensional model of the transmitting coil is constructed, and the high precision electromagnetic model of human body is carried out by using the finite difference time domain method. The distribution of specific absorption rate of different tissues is obtained. The safety analysis is carried out according to the International standard for electromagnetic safety of human body. The simulation results show that the artificial anal sphincter can stably and reliably supply energy to the internal device and it features favorable bio-electromagnetic compatibility. This study makes a firm theoretical foundation for the application of artificial anal sphincter.

Peng Zan, Chundong Zhang, Suqin Zhang, Yankai Liu, Yong Shao

The Role of Intelligent Computing in Load Forecasting for Distributed Energy System

The integration of renewable energy into the distributed energy system has challenged the operation optimization of the distributed energy system. In addition, application of new technologies and diversified characteristics of the demand side also impose a great influence on the distributed energy system. Through a literature review, the load forecasting technology, which is a key technology inside the optimization framework of distributed energy system, is reviewed and analyzed from two aspects, fundamental research and application research. The study presented in this paper analyses the research methods and research status of load forecasting, analyses the key role of intelligent computing in load forecasting in distributed energy system, and realizes and explores the application of load forecasting in practical energy system.

Pengwei Su, Yan Wang, Jun Zhao, Shuai Deng, Ligai Kang, Zelin Li, Yu Jin

Intelligent Control Methods of Demand Side Management in Integrated Energy System: Literature Review and Case Study

Demand side management (DSM) would become an important method to guarantee the stability and reliability of the innovative energy structure model, have received increasing attention. DSM is regarded as an integrated technology solution for planning, operation, monitoring and management of building utility activities. However, there are several problems and technical challenges on the research level of fundamental methodology, which causes difficulties for the practical application of intelligent DSM control strategy. Therefore, optimization would play a vital role in the implementation process of DSM. A real case study was presented to demonstrate how to relieve and solve the existing technical challenges by the application effective optimization strategy and methods of DSM. At last, several possible research directions, that application of intelligent methods in the development of DSM optimization techniques, were presented.

Yan Wang, Pengwei Su, Jun Zhao, Shuai Deng, Hao Li, Yu Jin

Optimal Design and Operation of Integrated Energy System Based on Supply-Demand Coupling Analysis

In this study, a bottom-up energy system optimization model is developed to assist the decision-making towards a sustainable energy system in the local area, while accounting for both supply-side and demand-side measures. The demand-side energy efficiency measures have been modeled as virtual energy generators, so as to be considered within a uniform optimization framework. The optimization model can provide feasible system configuration of both supply-side and demand-side appliances, as well as corresponding operating strategies, in terms of either economic performance or environmental benefit. As an illustrative example, a residential area located in Kitakyushu, Japan, is employed for analysis. The simulation results suggest that the combination of distributed energy resources and energy efficiency measures may result in better economic, energy and environmental performances. Moreover, it is technically and economically feasible to achieve more than 40% reduction in CO2 emissions within the local area.

Qiong Wu, Hongbo Ren

Modeling, Simulation and Control in Smart Grid and Microgrid


Control Strategies for the Microgrid Control System with Communication Delays

In this paper, two kinds of microgrid system architectures and the control approaches are studied. The proposed architectures are designed above a kind of communication network. The communication network characteristic is mainly described by network-induced delays which have greater influences on the control system performance. The network-induced delays in this paper is depicted by the inverse Gaussian distribution function. The proposed control strategies are implemented depending on the achitectures of themselves. The principle of event-triggered and droop-based approach are employed to restrain the different disturbances such as the break of main grid and insertion of new load. Some numerical examples are used to illustrated the effectiveness of the control approaches in this paper.

Weihua Deng, Pengfei Chen, Kang Li, Chuanfeng Li

Secondary Voltage Control of Microgrids with Distributed Event-Triggered Mechanism

This paper presents a secondary voltage control scheme with distributed event-triggered mechanism for multiple distributed generators in microgrids. First, to mitigate the over-provisioning of communication resources in microgrids, a distributed event-triggered mechanism is proposed. Then, based on the proposed triggering scheme, distributed secondary controllers are designed for distributed generators. Finally, simulation results demonstrate that with the adoption of the control strategy, the voltages of distributed generators are synchronised to their nominal values.

Jing Shi, Dong Yue, Shengxuan Weng

Frequent Deviation-Free Control for Micro-Grid Operation Modes Switching Based on Virtual Synchronous Generator

The virtual synchronous generator (VSG), which overcomes the impact of the traditional inverter without the moment of inertia to the power grid, improves the stability of the power system and has received extensive attention in Micro-grid. However, since the VSG uses the traditional active power-frequency droop control, there is a frequency deviation in island mode, which will adversely affect the load in Micro-grid. A frequent deviation-free control strategy based on VSG is proposed, i.e., the frequency proportional-integral (PI) module feedback is used to replace the traditional damping module. It will eliminate the frequency deviation of Micro-grid in island mode and realize the Micro-grid inverter to work in multi-mode control. The simulation results show that the effectiveness of the presented VSG based frequent deviation-free control strategy.

Yan Xu, Tengfei Zhang, Dong Yue

A Novel Data Injection Cyber-Attack Against Dynamic State Estimation in Smart Grid

Dynamic state estimation is usually employed to provide real-time operation and effective supervision of smart grid (SG), which has been also found vulnerable to a typical data injection cyber-attack submerged into big data. The attacks against dynamic state estimation can purposely manipulate online measurements to mislead state estimates without posing any anomalies to the bad data detection (BDD). Aiming at Kalman filter estimation, a novel data injection cyber-attack is proposed in this paper. Unlike the previous injection attack perfectly escaping the BDD, an imperfect attack targeting state variables is firstly investigated, and these targeted state variables are then determined by a new search approach, i.e., a $$\varepsilon $$-feasible injection attack strategy. Simulation results confirm the feasibility of the proposed attack strategy.

Rui Chen, Dajun Du, Minrui Fei

A Novel Combination of Forecasting Model Based on ACCQPSO-LSSVM and Its Application

This paper proposed a novel combination of prediction model based on Adaptive Cauchy and Chaos Quantum-behaved Particle Swarm Optimization (ACCQPSO) and Least Squares Support Vector Machine (LSSVM) to forecast the short-term output power more accurately. To improve the performance of QPSO, chaotic sequences are used to initialize the origin particles, and particle premature convergence criterion, Cauchy and Chaos algorithm are employed, which can effectively increase the diversity of population and avoid the premature convergence. The kernel parameters of LSSVM are optimized by ACCQPSO to obtain hybrid forecasting model. To verify the proposed method, the seven days actual data recorded in a wind farm located in Anhui of China are utilized for application validation. The results show that the proposed combinational model achieves higher prediction accuracy.

Nan Xiong, Minrui Fei, Sizhou Sun, Taicheng Yang

Research on Power Terminal Access Control Technology Supporting Internet Interactive Service in Smart Grid

With the continuous development of smart grid interactive business applications, the existing terminal access mechanism is difficult to ensure that all types of power terminals access to power information network security. Based on the idea of trusted computing and trusted network connection, this paper proposes a power terminal security access architecture for power interactive business from terminal security access and access control, terminal encryption and content filtering, and establishes architecture of supporting for interaction business from Internet and terminal security access, enhances the external network interactive services and terminal access authentication and access control capabilities in order to improve the security of terminal access to protect the strength of the Internet to ensure interactive services and terminal access to the trustworthy.

Song Deng, Liping Zhang, Dong Yue

Research on Model and Method of Maturity Evaluation of Smart Grid Industry

Smart grid has become the inevitable development trend of the modern power grid. The vigorous development of the smart grid led to the rise and development of the smart grid industry, seize the smart grid industry development opportunities, also has become one of the important choice of regional planning and construction. Scientifically reflecting the effect of regional development of smart grid industry of the conditions and the development of the industries to the region will guide regional smart grid industry planning, and encourage regional investment in the development of smart grid industry. This paper established smart grid industry maturity comprehensive evaluation index system from five aspects, the technical performance, industrial facilities, market environment, policy environment and social influence, to put forward to smart grid industry maturity evaluation algorithm, thus smart grid industry maturity assessment model is established, in order to provide reference for regional planning and smart grid industry.

Yue He, Junyong Wu, Yi Ge, Dezhi Li, Huaguang Yan

An Improved Multi-objective Differential Evolution Algorithm for Active Power Dispatch in Power System with Wind Farms

For the uncertainty of wind power and load, a reserve risk index is defined from minimum of load loss and maximum of utilizing wind power. Then, the index is introduced into optimizing for active power dispatch. Considering three indexes which consist of fuel cost, pollutant emission amount and the reserve risk index, a multi-objective optimization model for active power dispatch in power system with wind farms is established. For better solving model, an improved multi-objective differential evolution algorithm is proposed. This algorithm contains chaos initialization strategy, parameter adaptive strategy, dynamic non-dominated sorting strategy introduced to enhance the global searching ability. With the Pareto solution set, the entropy-based TOPSIS (technique for order performance by similarity to ideal solution) is adopted to sort the optimal solution set for the final scheme. The results and data analysis demonstrates the model is reasonable and the algorithm is valuable.

Shu Xia, Yingcheng Xu, Xiaolin Ge

Integration of the Demand Side Management with Active and Reactive Power Economic Dispatch of Microgrids

This paper presents a fully developed integration of the demand side management (DSM) into multi-period unified active and reactive power dynamic economic dispatch of the microgrids (MGs) combined with unit commitment (UC) to reduce the total operating cost or maximizes the profit with higher security. In the proposed optimization approach all consumers, such as residential, industrial, and commercial one can involve simultaneously in the DSM techniques. The shifting technique is applied to the residential load, while demand bidding programme (DBP) is applied to the industrial and commercial loads. The proposed optimal approach is tested on a low voltage (LV) hybrid connected MG including different types of loads and distributed generators (DGs). The results reveal that the proposed optimization approaches reduce the operating cost of the MG, while there are no impacts of the DSM on the profit.

Mohammed K. Al-Saadi, Patrick C. K. Luk, John Economou

Unit Commitment Dynamic Unified Active and Reactive Power Dispatch of Microgrids with Integration of Electric Vehicles

Electric vehicles (EVs) play a vital role in the reduction of emission of the greenhouse gases by reducing the consumption of fossil fuel. This paper presents a fully developed integration of the EVs with a security-constrained unified active and reactive power dynamic economic dispatch of microgrids (MGs) to minimize the total operating cost or maximizes the profit. The formulation of the overall optimization problem considers the reactive power production cost and relevant constraints, the environmental costs, and the battery degradation cost. A comprehensive set of constraints including active and reactive security constraints, limitation of the greenhouse gases constraints, and constraints relevant to the integration of the EVs with the MG are considered as well. The bi-directional penetration of the EVs with the MG is modelled and incorporated with unit commitment (UC) optimization problem. The results show that the proposed approach of the integration of the EVs with the MG reduces the total operating cost and increases the profit.

Mohammed K. Al-Saadi, Patrick C. K. Luk, John Economou

Optimal Design and Planning of Electric Vehicles Within Microgrid

Optimal allocation and economic dispatch of the distributed generators (DGs) and electric vehicles (EVs) are very important to achieve resilience operating of future microgrids. This paper presents a new energy management concept of interfacing EV charging stations with the microgrids. Optimal scheduling operation of DGs and the EVs is used to minimize the total combined operating and emission costs of a hybrid microgrid. The problem was solved using a mixed integer quadratic programming (MIQP) approach. Different kinds of distributed generators with realistic constraints and charging stations for various EVs with the view to optimizing the overall microgrid performance are investigated. The results have convincingly revealed that discharging EVs could reduce the total cost of the microgrid operation.

Mohammed Alkhafaji, Patrick Luk, John Economou

Security-Constrained Two-Stage Stochastic Unified Active and Reactive Power Management System of the Microgrids

This paper presents a developed robust two-stage scenario-based stochastic unified active and reactive power economic management system of microgrids (MGs) based on the unit commitment (UC) to minimize the total operating cost. The security constraints, the environmental costs, and the storage battery operating cost are considered in the proposed optimization approach. The mathematical stochastic models of the generation fluctuation of wind turbines (WTs) and photovoltaic panels (PV), and open market prices (OMPs) are developed and incorporated with UC optimization problem of the MG. The proposed stochastic approach is a two-stage optimization, where the first stage is the day-ahead scheduling based on the forecasted data, whereas the second stage mimics the real-time by considering the WT, PV, and OMP variability, where the UC is not changed in the second stage. The proposed optimization algorithm is tested on the low voltage connected MG. The results reveal that the feasible solution can be obtained for all scenarios.

Mohammed K. Al-Saadi, Patrick C. K. Luk

Charging and Discharging Strategy of Electric Vehicles Within a Hierarchical Energy Management Framework

As the number of EVs is increasing modern methods are required to understand their impact to the power grid (operators and users). In order to reduce/manage fluctuations on voltage stability and angle stability there is a need for a management control strategy. This paper presents an energy management concept of Charging Station System (CSS) to charge or discharge power of EVs in different situations while retaining system integrity. A suitable objective function is formulated of frequency deviation and voltage deviation on the optimal operation of the charging station are evaluated by formulating and solving the optimisation problem using mixed integer linear programming. The results show that EVs act as a regulator of the microgrid which can control their participation role by discharging active or reactive power in mitigating frequency deviation and/or voltage deviation. The optimisation algorithm is evaluated by formulating and solving the optimisation problem using mixed integer linear programming. Case studies are used to show the viability of the proposed energy management concept.

Mohammed Alkhafaji, Patrick Luk, John Economou

Optimization Methods


Optimization Allocation of Aerospace Ground Support Vehicles for Multiple Types of Military Aircraft

As an important class of support resources, the allocation of aerospace ground support vehicles (AGSV) has important impact on the sortie generation rate of multiple types of military aircraft (MTMA). In this paper, a general queueing model of AGSV has been built to describe the features of the support process of MTMA using the multi-class closed queueing networks. To satisfy constraints on each sortie generation rate (SGR) of MTMA and get good economic benefits, an optimization allocation model of AGSV has been developed to minimize the total value of AGSV. Based on mean value analysis and marginal analysis, a solving algorithm determines the numbers of AGSV at each station. The results of a case study show applicability of the optimization model.

Fuqin Yang, Jinhua Li, Mingzhu Zhu

Multi-level Maintenance Economic Optimization Model of Electric Multiple Unit Component Based on Shock Damage Interaction

In order to simulate the reliability evolution process of Electric Multiple Unit (EMU) components under external shock and improve maintenance economy. The multi-level preventive maintenance method is established and the influence of maintenance period and allocation of multi-level imperfect maintenance on the maintenance economy are discussed respectively. Numerical experiments show that the multi-phase preventive maintenance model can reduce the maintenance cost rate. The analysis of bi-level imperfect maintenance capacity indicates that two-level preventive maintenance can extend the mileage of four-level preventive maintenance and three-level preventive maintenance can reduce the maintenance cost rate. Finally, some recommendations for the allocation of maintenance efforts are provided according to the different railway route features.

Hong Wang, Yong He, Lv Xiong, Zuhua Jiang

A Composite Controller for Piezoelectric Actuators with Model Predictive Control and Hysteresis Compensation

Piezoelectric actuators (PEAs) are ubiquitous in nanopositioning applications due to their high precision, rapid response and large mechanical force. However, precise control of PEAs is a challenging task because of the existence of hysteresis, an inherent strong nonlinear property. To minimize its influence, various control methods have been proposed in the literature, which can be roughly classified into three categories: feedforward control, feedback control and feedforward-feedback control. Feedforward-feedback control combines the advantages of feedforward control and feedback control and turns into a better control scheme. Inspired by this strategy, a composite controller is proposed for the tracking control of PEAs in this paper. Specifically, the model of PEAs is constructed by a multilayer feedforward neural network (MFNN). This model is then instantaneously linearized, which leads to an explicit model predictive control law. Then, an inverse Duhem hysteresis model is adopted as a feedforward compensator to mitigate the hysteresis nonlinearity. Experiments are designed to validate the effectiveness of the proposed method on a piezoelectric nanopositioning stage (P-753.1CD, Physik Instrumente). Comparative experiments are also conducted between the proposed method and some existing control methods. Experimental results demonstrate that the root mean square tracking error of the proposed method is reduced to 16% of that under the previously proposed model predictive controller [16].

Ang Wang, Long Cheng

Computational Methods for Sustainable Environment


Numerical Investigation of the Environment Capacity of COD, Inorganic Nitrogen and Phosphate in the Bohai Bay

An ocean dynamic model is used to simulate the tides and currents in the Bohai Bay. Model results are validated by comparing with observations. Furthermore, the conservative tracer is used to estimate the water exchange rate of the Bohai Bay, and it is found that about 62% of the seawater is transported out of the bay annually. At last, the grade 2 quality of the seawater is taken as the criteria to investigate the environment capacity of three major pollutants. It is found that the static capacity of COD, inorganic nitrogen and phosphate is about 3.999 × 105, 3.999 × 104 and 3.999 × 103 t/a, respectively, if the water exchange is not considered. furthermore, the process-controlled environment capacity for three pollutants can be 6.478 × 105, 6.478 × 104 and 6.478 t/a, respectively, and the consequence-controlled environment capacity may be as high as 1.052 × 106, 1.052 × 105 and 1.052 × 104 t/a, respectively.

Hao Liu, Zhi-kang Zhang

An Artificial Neural Network Model for Predicting Typhoon Intensity and Its Application

Considering that the typhoon intensity’s statistical predictors have the characteristics of inaccuracy, incompleteness and uncertainty, and the optional factors are factors are usually lots in a practical application, but the predictive ability will decline if using too many factors in a model, and may also lost the important information by choosing the inappropriate factorsQuery. Latitude and longitude of storm center, minimum central pressure, maximum wind speed near the storm center were chosen to be predictors, and a neural network model for predicting typhoon intensity was established by using every 6 h of current and former 18 h of these information directly. In this study, 61-year data set from 1949 to 2009 was used to train the networks, and 5-year data set from 2010 to 2014 was used to test the trained network. Compared with other typhoon predicting models, and results showed that the model has obtained a good predicting accuracy.

Ruyun Wang, Tian Wang, Xiaoyu Zhang, Qing Fang, Chumin Wu, Bin Zhang

Analysis of Power Spectrum Feature Based on Slurry Noise in Electromagnetic Flowmeter

As an essential part of measuring technology, flowmeters have been widely used in industrial production. Accurate flow metering will not only improve the quality of products, promoting economic efficiency and management level, but lays foundation for the assessment of energy saving and environmental sewage discharging. Electromagnetic flowmeter is suitable for slurry flow measurement since it has high reliability, strong corrosion resistance, high measurement precision, and no stopped medium components in the measuring pipe. In order to improve the measurement of slurry noise problem, this paper will analyze the power spectrum of the slurry noise, and then find the relationship between slurry noise and excitation frequency as to provide theoretical basis for frequency switch of the variable frequency electromagnetic flowmeter.

Jie Chen, Qiong Fei, Bin Li, Xiaojie Zheng

A Two-Stage Agriculture Environmental Anomaly Detection Method

In order to process abnormal problems of massive distributed greenhouse environmental data, a novel anomaly detection algorithm based on the combination of Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) is proposed and realized under the Spark framework, which is utilized to detect the environmental anomaly during crop growth. At the first stage, SVM is adopted to classify the data, Spark framework is utilized to solve optimization problem iteratively; at the second stage, GMM is used to do clustering on the classified data respectively. Spark framework is utilized to update the models internationally until stable, during every iteration. Map phase implements the distribution of the sample points to the models. Reduce phase renew the numbers of models and the parameters. Finally, the detection of environmental anomaly is completed by taking advantages of the clustering result. The results show that the proposed approach can be well applied to actual production.

Lili Wang, Yue Yu, Li Deng, Honglin Pang

Building a Virtual Reality System for Intelligent Agriculture Greenhouse Based on Web3D

The inevitable trend of agricultural development in China is to realize real-time monitoring, visualization and management of intelligent agriculture greenhouse. This paper presents a virtual reality system that is developed for an intelligent agricultural greenhouse using Web3D. Both virtual reality and image matching technology are integrated to construct such a VR system. The basic roaming interactive function of the system is realized using 3DS MAX modelling technology and virtual reality platform (VRP). An improved SIFT algorithm is proposed to complete panoramic image mosaic, which is on the basis of SIFT feature matching algorithm combined with Harris algorithm. Finally, the 3D visualization of agricultural greenhouse, the data management and real-time interactive control are realized and tested.

Qun Huang, Li Deng, Minrui Fei, Huosheng Hu

A Green Dispatch Model of Power System with Wind Energy Considering Energy-Environmental Efficiency

With rising pressure caused by decreasing natural resources, there emerges stronger call for environmental protection and sustainable development. As a kind of environmental-friendly energy, wind power has been increasing in capacity around the world in recent years. As more and more wind power gets connected with the grid, its impact on power dispatch should also be considered. Based on traditional power system optimized dispatch, this paper introduces the energy-environmental efficiency concept into construction of a green dispatch model for wind incorporated power systems. The strategy takes both minimum resource consumption and best energy-environmental efficiency as indexes to assess the optimization of wind power incorporated systems from an environmental-friendly point of view. Fuzzy technology is adopted along with the tabu search-based PSO algorithm to solve the problem. It is proven that the proposed model is reasonable and of good practicality.

Daojun Chen, Liqing Liang, Lei Zhang, Jian Zuo, Keren Zhang, Chenkun Li, Hu Guo


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