Table of Content

28 February 2022, Volume 0 Issue 2

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Research & Analysis

Power Grid Fault Location Algorithm Based on Protection State Information and Agglomerated Hierarchical Clustering

HE Sen, CHEN Yisen, ZHANG Hourong, ZHENG Wenjian, SONG Yunhai, SHANG Jianing, CUI Mandi, WANG Qi, CHANG An, LAI Guanglin

2022, 0(2):  1-5.  doi:10.16628/j.cnki.2095-8188.2022.02.001

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Aiming at the problem of insufficient accuracy and fault tolerance due to low information utilization of existing fault location algorithms,a fault location algorithm based on protection state information and Agglomerative Hierarchical Clustering (AHC) is proposed.Firstly,the state information of circuit breakers and protective devices in the power grid is obtained by Intelligent Electronic Device (IED) to construct the fault feature vector.Then,AHC algorithm is used to cluster the feature vectors,and the faulty elements are obtained according to the association between elements and IEDs.Finally,it is compared with the existing power grid fault location algorithms based on fuzzy C-means and improved spectral clustering.The results show that the proposed algorithm has the advantages of not necessary to predict the number of clusters in advance,more stable fault location results,and improved accuracy and fault tolerance for IED information.

Fault Prediction of Intelligent Electricity Meter Based on Multi-Classification Machine Learning Model

LI Ning, ZHANG Wei, GUO Zelin, YUAN Tiejiang, HAN Xinlei

2022, 0(2):  6-11.  doi:10.16628/j.cnki.2095-8188.2022.02.002

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In view of the characteristics of large scale,high dimension,error and abnormal data of smart electricity meter fault data,a fault prediction method of smart electricity meter based on multi-classification machine learning model is proposed.The missing values and outliers are replaced in the original data set by using normal distribution completion and box diagram method.By calculating the correlation coefficient between feature attributes and fault types,the uncorrelated features are eliminated and the feature subset is formed.To solve the problem of unbalanced fault data,a mixed sampling strategy is built.The prediction accuracy of three typical machine learning algorithms to process the fault data of smart electricity meters is calculated,and the confusion matrix is constructed.Considering the prediction ability of each classifier,the multi-classifier fusion decision function is constructed.Finally,the effectiveness of the proposed method is verified by using public data sets and actual electricity consumption data as samples.

Optimization of Multi Microgrid Game Method Under Shared Energy Storage Mode

ZHENG Hailin, WEN Buying, ZHU Zhenshan, WENG Zhimin

2022, 0(2):  12-20.  doi:10.16628/j.cnki.2095-8188.2022.02.003

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The high investment cost of energy storage is the main obstacle to its commercial development.Through the energy storage aggregators to coordinate the operation of energy storage equipment,the utilization rate of energy storage is improved and the cost is reduced.Firstly,the cost of adjustable flexible resources such as thermal power units,charging stations and interruptible loads in microgrid and the cost sharing of shared energy storage are comprehensively considered.Aiming at maximizing the benefits of all parties,the game optimization operation model between microgrid and shared energy storage aggregator is constructed.Secondly,the Multi-Agent Reinforcement learning method is used to solve the multi-agent game problem,and KL divergence is introduced to optimize the agent learning rate and improve the convergence of the algorithm.Finally,taking three adjacent microgrids as examples,the economic benefits of each subject are improved under the shared energy storage mode,which verifies the superiority of the mode and the effectiveness of the algorithm improvement.

Research on Intelligent Control of Large Users’Electricity Load Considering Two-Way Interaction

ZHANG Lin, YIN Xiaoqiu

2022, 0(2):  21-26.  doi:10.16628/j.cnki.2095-8188.2022.02.004

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The peak load and peak-to-valley difference of my country’s power grid are growing rapidly,and the peak-shaving capacity is insufficient.Therefore,power demand-side management should be actively implemented.By carrying out research on the demand-side interaction mechanism,a class of load control technology for cluster buildings and large buildings and interactive technology for the power load of cluster buildings are proposed to meet the demand-side power consumption monitoring and demand-side power consumption of the supply side.The goal of distribution control is to solve the problem of itemized time-sharing monitoring and control of electricity in large users' electricity load,the coordination of wide-area and multi-zones,as well as the coordination and optimization of demand-side management.

Research on Parallel Transfer Technology of Power and Signal in MC-WPT System

YIN Yong, WANG Chengliang, XIAO Yuhua, GAO Yuan

2022, 0(2):  27-33.  doi:10.16628/j.cnki.2095-8188.2022.02.005

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With the development of wireless power transfer technology,its application range is expanding.In order to improve the robustness of the system,to realize high-performance power transfer and signal transfer,more and more applications require the realization of communication function.Based on the technology of magnetically coupled wireless power transfer,the framework of power signal parallel transmission system is designed according to the specific application scenarios.LCC-S structure is selected for power transfer compensation topology.A pair of special coils are added to the traditional power transfer coupling mechanism as communication channel to realize the parallel transfer of power and signal.The feasibility of the system is verified from the point of simulation and experiment.

Simulation Technology Application

Modeling and Dynamic Matrix Control Design of Solid State Electric Heat Storage System with Time Delay Based on Parameter Identification

XING Zuoxia, SU Jian, FU Qitong, LIU Yang, WANG Xiaoqi

2022, 0(2):  34-41.  doi:10.16628/j.cnki.2095-8188.2022.02.006

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The solid electric heat storage device of energy storage equipment is to use the abandoned wind energy in the valley period to store heat energy,which plays an important role in the peak load regulation of power grid.In order to solve the problem that the traditional PID heating control system has large time delay,large overshoot and long-time oscillation,which leads to the temperature fluctuation of heating water is too large to meet the heating demand,this paper proposed a solid electric heat storage time-delay system modeling and dynamic matrix control design based on parameter identification.The parameters of the identification model are obtained by the least square method,and the dynamic matrix control strategy of air volume and water temperature of solid electric heat storage cycle is designed.The results show that the dynamic matrix control algorithm has smaller overshoot,stronger anti-interference ability,better effect and better dynamic performance.

Thermal Simulation and Design Optimization of Multi-Channel SiC Solid-State Power Controller

LAI Yaokang, WANG Haonan, CAO Yufeng, WANG Zicheng, YE Xuerong, ZHAI Guofu

2022, 0(2):  42-45.  doi:10.16628/j.cnki.2095-8188.2022.02.007

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With the development of SiC MOSFET,the multi-channel SiC solid-state power controller with this device as the core part is gradually applied in the field of high-power density.Due to its higher temperature rise and extreme ambient temperature,the demand for thermal management of products has also increased.Therefore,an accurate assessment of the internal heat distribution of the product is a prerequisite for the design of a high-performance multi-channel solid-state power controller.This article took the 12-channel solid-state power controller with rated voltage of 270 V and rated current of 240 A as the object,analyzed its thermal failure mechanism,and established a simplified finite element model.The steady-state and transient thermal simulations and verifications of the 120 A and 180 A derating working conditions were carried out respectively.The thermal simulation on 240 A full-load working conditions was performed,and thermal design optimization was made based on the simulation results and actual conditions.Finally,the full-load test of the optimized product was carried out to verify the correctness of the thermal simulation and design optimization.

Experimental Simulation of Bimetallic Strip Thermal Flexivity Specialty in Miniature Circuit Breaker

PAN Qingyuan

2022, 0(2):  46-52.  doi:10.16628/j.cnki.2095-8188.2022.02.008

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This paper studied the thermal tripping bimetallic strip in miniature circuit breaker (MCB),analyzed the thermal flexivity specialty from aspects of theoretical calculation,experiment and digital simulation,respectively.A new method was proposed for bimetallic strip optimization design,and a platform was established to simulate the thermal flexivity of the bimetallic strips.A set of special fixture was developed and designed for bimetallic strip flexivity performance experiments,the experiment platform and the method were improved.Using numerical simulation instead of part of preliminary experiments,with DOE,the optimal solution of design parameters could be solved rapidly,therefore the investment and time cost were both saved.In addition,the simulation platform could also be used for the collaborative design of operating mechanism parameters,of which the verification objective is tripping force and tripping stroke.

Generation Automation

Short-Term Load Forecasting Method of Distribution Transformer Based on EMD-Stacking-MLR

YANG Xiu, HU Zhongyu, TIAN Yingjie, XIE Haining

2022, 0(2):  53-62.  doi:10.16628/j.cnki.2095-8188.2022.02.009

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The traditional short-term load forecasting methods are mostly data-driven machine learning methods,and the application scenarios are mostly macroscopic city/county regional total load forecasting.In the face of the distribution transformer load,the prediction effect is obviously insufficient.In this regard,a load forecasting method based on EMD-Stacking-MLR is constructed.Firstly,the distribution transformer load data are decomposed into finite intrinsic mode function components from high to low frequency by empirical mode decomposition method.The high and low frequency components are divided according to the sample entropy value.Subsequently,Stacking multi-model fusion method and multiple linear regression method are used to predict the high and low frequency components respectively.Finally,the final distribution transformer prediction load curve is obtained by superposition of every components prediction results.Through experimental verification,the results show that this method has achieved remarkable results in improving the load forecasting accuracy and model generalization ability.

Reliability Improvement of Distribution Network with Distributed Generation Sources and Diversified Loads

WANG Yunhui, ZHENG Qiangren, GUO Miao, XIAO Huanchun, SI Cong, CHEN Wanxi

2022, 0(2):  63-67.  doi:10.16628/j.cnki.2095-8188.2022.02.010

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Intelligentization is the foundation of the development of the distribution network and one of the main directions of the development of the distribution network.The access of distributed power sources and diversified load are bound to have an impact on the reliability of the distribution network.The impact of distributed power sources and diversified loads on the reliability of distribution network power supply was mainly analyzed.The strategies and methods to improve rebiability of distribution automation intervention were summarized and studied.After the intervention of distribution automation,the calculation model of reliability in different modes was built and the effectiveness of the reliability calculation results was evaluated based on the analytic hierarchy process and the FMEA failure mode consequence analysis method.Finally,it elaborated on the further issues that need to be considered in the reliability calculation method of the distribution network with distributed power sources and diversified loads,and the direction of standardization for popularization and application.

Power Quality

Application of Wavelet Neural Network Based on Chaotic Particle Swarm Optimization Algorithm in Power Quality Disturbance Signals Classification

WU Juzhuo, CHEN Shuyuan, NIU Haiqing, LU Xiaopeng

2022, 0(2):  68-73.  doi:10.16628/j.cnki.2095-8188.2022.02.011

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In order to classify power quality disturbance signals more effectively,the wavelet transform and neural network are organically combined to build a four-layer wavelet neural network model in this paper.Besides,the chaotic is embedded into particle swarm optimization algorithm to improve the convergence speed and accuracy of the network model training based on the property of chaotic.Then the trained network model is used to classify the normal voltage and several common power quality disturbances.The classification result shows that the wavelet neural network optimized by chaotic particle swarm optimization algorithm can effectively classify power quality disturbances,and has the advantage of strong interference resistance and good stability.Meanwhile,compared with particle swarm optimization algorithm and BP algorithm,classifying the power quality disturbances based on chaotic particle swarm optimization algorithm has the higher accuracy rate of classification.

Voltage Violation Risk Assessment and Pre-Control in Active Distribution Network Considering Uncertainty

LIAO Jianbo, CHEN Bo, CHEN Yuanwei, YU Wei, CHEN Jiancong, LIN Dazeng, ZHANG Lu, NAN Dongliang

2022, 0(2):  74-82.  doi:10.16628/j.cnki.2095-8188.2022.02.012

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Aiming at the voltage violation risk caused by the uncertainty of DGs and loads in short-term operation,a voltage violation risk assessment and pre-control method of active distribution network (ADN) is proposed.The voltage violation risk assessment index is established.The stochastic power flow solution based on cumulant and gram-charlier series expansion is applied to calculate the risk index.When the risk exceeds the threshold,pre-control is carried out to improve the system operation security.Considering the active and reactive power of distributed energy resources,flexible loads management and traditional reactive and voltage equipment,a voltage violation risk pre-control model based on multi-level active management is proposed.Based on harmony search algorithm,a model solving strategy is formulated.The proposed method is applied to evaluate and pre-control the voltage violation risk of ADN,which realizes the accurate identification of key weak nodes and effective risk control.

New Algorithm for Three-phase Four-Wire Unbalance Regulators with Active Compensation Current Exceeding Capacity of Equipment

SUN Lingxi, WANG Haixin, HUANG Haihong

2022, 0(2):  83-87.  doi:10.16628/j.cnki.2095-8188.2022.02.013

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Under the condition that the load of the grid is too large and the unbalance is high,the three-phase four-wire unbalance regulator had the phenomenon that the active compensation current exceeds the rated capacity of the equipment.The common method to solve this problem is to limit the amplitude,but this method can distort the active compensation current waveform and cause voltage fluctuations on the DC side of the equipment because the sum of the three-phase active currents flowing into the equipment is not zero.This paper introduces the working principle of the three-phase four-wire unbalance regulator,and analyzes the shortcomings of the limiting method,and finally proposes a new active current compensation scheme,i.e.,the active compensation current proportional reduction method.The correctness of this method is proved by mathematical derivation.Finally,it is verified through simulation and experiment that this method can avoid the drawbacks of the limiting method and maximize the regulating effect of the device.

Voltage and Reactive Power Control Strategy of Energy Storage Power Stations in Large New Energy Enriched Areas

FU Meiping, MAO Jianrong, SONG Rui, WANG Guanghui, FU Guobin

2022, 0(2):  88-94.  doi:10.16628/j.cnki.2095-8188.2022.02.014

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In order to improve the voltage and reactive power support capacity in new energy enriched areas,a double-layer voltage and reactive power coordinated control strategy for energy storage power stations combined with new energy is proposed.Firstly,on the minute-level time scale,the voltage and reactive power optimization at the regional level aims is the minimum voltage deviation and network loss,and the improved particle swarm optimization is used to solve the regional optimal voltage distribution.For the second-level adjustment of node voltage fluctuations,the deviation recovery control strategy based on voltage and reactive power sensitivity is adopted.Through the coordination of two time-scale control strategies,the various reactive resources such as energy storage and new energy is used rationally.Secondly,at the power station level,the voltage and reactive power relationship of the energy storage power station is analyzed,and the multi-source coordinated control of the regional regulation command is carried out considering the state of charge of the energy storage.Finally,the effectiveness and practicability of the proposed strategy are verified through the example analysis.