Table of Content

30 March 2023, Volume 0 Issue 3

Previous Issue    Next Issue

Research & Analysis

Auxiliary Service Strategy of Virtual Power Plant Participating in Peak Shaving Based on Improved Q-Learning

CHEN Conglei, ZHONG Jihan, CAO Xiaobo, LUO Xiaodong, XU Jun

2023, 0(3):  1-10.  doi:10.16628/j.cnki.2095-8188.2023.03.001

Asbtract ( 274 )   HTML ( 5)   PDF (1887KB) ( 219 )  

Figures and Tables | References | Related Articles | Metrics

In the construction of the new power system,the grid connection and preferential consumption of large-scale photovoltaic power may lead to grid congestion and aggravate the peak shaving pressure of thermal power units,which will worsen their operating conditions and increase their costs.Aiming at this problem,the virtual power plant participation system in the day-to-day in-depth peak shaving auxiliary service strategy is proposed.First,the peak shaving characteristics and operating costs of the virtual power plant composed of electric vehicle groups are analyzed and calculated.According to the coal consumption and emission data of thermal power units, the coal consumption for power supply is calculated and the environmental protection indicators are established.Secondly,based on the operating costs and indicators,taking the peak shaving margin of thermal power units as the optimization goal, the simulated annealing improved Q-learning is used to solve the deep peak shaving capacity and cost allocation. The results show that the participation of virtual power plants in system peak shaving can improve the flexibility of peak shaving, reduce operating costs, and relieve the peak shaving pressure of thermal power units.

Research on Generator Tripping Control Strategy Based on Deep Reinforcement Learning

LU Hengguang, LIN Bilin, WEN Buying

2023, 0(3):  11-15.  doi:10.16628/j.cnki.2095-8188.2023.03.002

Asbtract ( 228 )   HTML ( 3)   PDF (1166KB) ( 175 )  

Figures and Tables | References | Related Articles | Metrics

The power system will enter an emergency state after being greatly disturbed.The emergency control measures must be taken in time to restore the system to stable operation.Generator tripping control is the most effective and common control measure to maintain system stability.Aiming at the problem of fault mismatch in practical application of the traditional control method based on cure table,a decision method of power system transient stability generator tripping control based on deep reinforcement learning is proposed.Firstly,the deep deterministic policy gradient (DDPG) algorithm is introduced.Every element of the algorithm is redesigned in combination with the equal area criterion.Secondly,the decision model of generator tripping control based on DDPG algorithm is established.Finally,using PSA-BPA and Pycharm software,the generator tripping control simulation models of the single machine-infinite system and an IEEE39 node system are established.The effectiveness of the proposed method is verified by an example.

Distributed Frequency Control Strategy for Power Systems Considering Influence of Inertia

ZHU Chen, ZHENG Junfeng, FANG Xing, FU Xiang, CHEN Baiyuan, ZHANG Yang, HUANG Jiyuan

2023, 0(3):  16-21.  doi:10.16628/j.cnki.2095-8188.2023.03.003

Asbtract ( 232 )   HTML ( 10)   PDF (1969KB) ( 165 )  

Figures and Tables | References | Related Articles | Metrics

With the rapid development of new energy,the electric permeability of power system increases gradually,and the system presents the characteristics of low inertia.Virtual inertia control is one of the main ways to improve the frequency support ability of low inertia systems,but it will change the equivalent inertia level of the system and affect the frequency adjustment process.In order to solve these problems,the distributed frequency control strategy considering the influence of inertia is proposed.The power instruction compensation prediction output model of the virtual inertia controller is introduced.The distributed model prediction frequency controller based on the model is designed.The simulation results show that the proposed control strategy can effectively improve the frequency control performance and restrain the excessive adjustment of wind turbines in frequency control to a certain extent.

Control Architecture and Peak Shaving Strategy Under Multi-Station Integration

CHEN Yufeng, LIANG Siwei, WANG Jiahua

2023, 0(3):  22-27.  doi:10.16628/j.cnki.2095-8188.2023.03.004

Asbtract ( 219 )   HTML ( 3)   PDF (2280KB) ( 118 )  

Figures and Tables | References | Related Articles | Metrics

In order to solve the problem of coordinated control and operation scheduling in the scene of multi-station integration,the control architecture and peak shaving operation strategy in multi-station integration is proposed.Firstly,according to the control response characteristics of each power supply,a control architecture with strong compatibility is designed.Secondly,according to the multi-station integration the peak shaving control objective of minimum peak-valley difference,combined with the designed control architecture,a rolling optimization peak shaving strategy is propose.s PSO algorithm is used to solve the peak shaving optimization model.Finally,the effectiveness of the rolling optimization peak-shaving strategy in the multi-station integration is verified by an example analysis.

Distribution Automation

Optimal Partitioning of Source-Load Matching Power Grid Based on Improved K-Means Algorithm

ZHOU Gang, CAO Chenrun, LI Ruifeng

2023, 0(3):  28-32.  doi:10.16628/j.cnki.2095-8188.2023.03.005

Asbtract ( 256 )   HTML ( 4)   PDF (1175KB) ( 179 )  

Figures and Tables | References | Related Articles | Metrics

In view of the limitations of the heuristic method based on experience in the power grid partitioning problem,considering the matching characteristics between the new energy and the load in the power grid partitioning problem,the measurement index of the power grid optimal partitioning is established.Based on the source-load matching degree,theoptimal partitioning of power grid is realized by solving the source-load matching degree coefficients of each region.In the traditional K-means algorithm,the sum of error squares is introduced to screen the best clustering value to avoid the adverse impact of the clustering model on the analysis results.The calculation example uses the operation data of six 500 kV substations in a area to analyze and calculate the source-load matching coefficient under each main transformer.According to the coefficient,the power grid is partitioned.The results show that the proposed algorithm can effectively balance the short circuit capacity between partitions.The improved algorithm has significantly improved the overall clustering effect.

Research on Optimization Method of Low Voltage Substation Area Planning Combined with Flexible Operation Strategies

XIAO Jianhua, YANG Lingling, LIU Yong, YUAN Qiushi

2023, 0(3):  33-39.  doi:10.16628/j.cnki.2095-8188.2023.03.006

Asbtract ( 238 )   HTML ( 1)   PDF (1384KB) ( 140 )  

Figures and Tables | References | Related Articles | Metrics

The low-voltage substation area is the link closest to the end user in the power system.Firstly,the environmental benefits of new energy power generation are analyzed by applying the theory of environmental economics and the pollutant emission standard of thermal power generation.The power generation characteristics of distributed photovoltaic are studied and the power consumption scenarios of its access to low-voltage power substations are analyzed.Then,taking the interruptible load in the low pressure substation area as the flexible resource for regulation and control,aiming at the maximization of social benefits,the two-level optimization model of operation and planning in the low voltage substation area is established.Finally,the example is given to illustrate that the proposed model can effectively improve the voltage quality of the substation area and the ability to cope with the fluctuation of new energy output.

Combination Prediction Method and Application Based on Substation Area 10 kV Distribution Load

ZHAO Zekun

2023, 0(3):  40-45.  doi:10.16628/j.cnki.2095-8188.2023.03.007

Asbtract ( 255 )   HTML ( 3)   PDF (1595KB) ( 206 )  

Figures and Tables | References | Related Articles | Metrics

The problem of heavy line overload not only affects the service life of power supply equipment,but also easily leads to a series of safety accidents.In order to achieve the goal of effective early warning of the problem of heavy overload of 10 kV lines,starting from the power consumption information collection substation area,the combination prediction model of 10 kV distribution load based on genetic neural network and adaptive weight is built.Then combinating with the line change relationship,the distribution load is predicted and the load situation of each 10 kV distribution line is grasped one day in advance.Taking the measures such as adjusting the power consumption scheme or load transfer in time for the distribution line with heavy overload risk,the heavy overload pressure of the line can be effectively relieved and the safe and stable operation of the distribution network can be ensured.Experiments show that the proposed model has high prediction accuracy and application value.

Comprehensive Evaluation Method of Active Distribution Network Operation Health Level Based on Massive Operation Data

ZHU Kai, LI Yaqing, HU Zhenhua, LIU Fanglei, WANG Zhenhua, WANG Hang, XING Haijun

2023, 0(3):  46-53.  doi:10.16628/j.cnki.2095-8188.2023.03.008

Asbtract ( 265 )   HTML ( 4)   PDF (954KB) ( 130 )  

Figures and Tables | References | Related Articles | Metrics

When the traditional distribution network passively deals with faults or abnormal operating conditions,it lacks the early warning,real-time monitoring and rolling inspection of the grid risks or the hidden dangers such as the grid structure weakness points.In order to make the business model of active distribution network potential risk detection real-time and comprehensive,the comprehensive evaluation method of active distribution network operation health level based on massive operation data is provided.Considering the risk level of the real-time operation of the distribution network and the potential risk caused by the structural defects of the distribution network itself,it comprehensively reflects the healthy operation level of the distribution network.The problems of the traditional distribution network evaluation system are sdlved,such as the cumbersome setting of evaluation indicators,the single evaluation characteristics and the low reliability of evaluation results.

Detection & Test

Capacitive Component Leakage Current Compensation Method Based on Multilayer Fully Connected Neural Network

ZHOU Xingyu, WU Guichu, WU Ziran, LI Quanfang

2023, 0(3):  54-61.  doi:10.16628/j.cnki.2095-8188.2023.03.009

Asbtract ( 226 )   HTML ( 2)   PDF (1787KB) ( 141 )  

Figures and Tables | References | Related Articles | Metrics

The capacitive component leakage current (CCLC) commonly exists in power distribution systems.However,it significantly interferes the leakage current measurement and reduces the accuracy of electrical fire monitoring.The leakage current test platform is built to simulate the actual circuit condition,and a data acquisition system is designed to display and acquire the data of four voltage modes and four load modes under the resistive leakage and capacitive leakage conditions.A multilayer fully connected BP neural network is used to eliminate the CCLC by predicting the resistive component leakage current (RCLC).The test results show that the prediction error of the RCLC reaches only 1.08%,which can prove that the methd can effectively identify the RCLC and improve the accuracy of electrical fire alarming.

Research and Application of Switchgear Temperature Load Based on Neural Network Algorithm

XU Weidong, HE Wenzhi, LIAO Zhaoyi, LIU Qinfeng

2023, 0(3):  62-68.  doi:10.16628/j.cnki.2095-8188.2023.03.010

Asbtract ( 241 )   HTML ( 4)   PDF (1394KB) ( 227 )  

Figures and Tables | References | Related Articles | Metrics

Abnormal changes in temperature and load current of high-current switchgear may lead to equipment defects and affect normal operation.In order to effectively monitor the temperature of the switchgear,the Ansoft electro-thermal simulation module is used to analyze the temperature field changes of key components in the switchgear.The data of temperature changes under different environmental conditions is obtained.The relationship between the temperature sensor and the distance and position of the hot spot is mastered.Finally,the temperature load curve under different current is obtained through the test.The accuracy of the research results is verified by combining the neural network prediction method with the field test.The results can provide the guidance for the on-site operation and maintenance personnel of the substation to install the temperature sensor.

Research on Compensation Technology for Improving Efficiency of High Current Temperature Rise Test System

WANG Xiaolong, LIU Aihua, LIU Xueji

2023, 0(3):  69-72.  doi:10.16628/j.cnki.2095-8188.2023.03.011

Asbtract ( 235 )   HTML ( 3)   PDF (974KB) ( 144 )  

Figures and Tables | References | Related Articles | Metrics

In the temperature rise test of high current switchgear,in order to solve the problem that the output current of the test system can not meet the requirements due to the large inductance effect,the impedance characteristics of the temperature rise test system and the high current test circuit are tested and calculated by applying the small current.The overall inductive component data of the circuit is obtained.The appropriate capacitance compensation solution is proposed.The analysis and calculation method of the parameter selection,grading switching and protection setting of the compensation capacitor bank is given.After taking the capacitive compensation measures,the output current of the temperature rise test system meets the test requirements of the tested high current switchgear.The proposed scheme has the advantages of good effectiveness,reliable technology and low cost,and does not need to change the capacity of the pressure regulating and flow raising device of the original system.

Abnormal Power Consumption Mode Detection Based on Empirical Mode Decomposition and Multi-View Clustering

WANG Jianyuan, LIU Kechen

2023, 0(3):  73-80.  doi:10.16628/j.cnki.2095-8188.2023.03.012

Asbtract ( 237 )   HTML ( 5)   PDF (1604KB) ( 200 )  

Figures and Tables | References | Related Articles | Metrics

In order to solve the low detection efficiency of the existing abnormal power consumption detection methods,the anomaly detection method based on empirical mode and multi view clustering is proposed.Following the process of "empirical mode decomposition-dimensional constraints-multi-view clustering-horizontal detection-vertical detection" and combining the multi-view clustering with the preliminary criteria,the detection rate is significantly improved.In the anomaly detection algorithm,the grid-based entropy outlier factor (Grid-EOF) algorithm is proposed.A new criterion is given based on the longitudinal detection,which can improve the detection rate of users with unknown electricity theft.Finally,it is verified by the measured data of smart meters of the State Grid of China.The results show that the introduction of multi-view clustering,improved algorithm and longitudinal detection can effectively improve the detection rate and accuracy of the anomaly detection model.