基于图卷积网络的电能质量评估

doi:10.16180/j.cnki.issn1007-7820.2024.09.007

Abstract

Abstract:

The increasingly widespread use of new power equipment has brought new disturbances to the power system and has placed increasing demands on power quality. In order to make full use of the power quality indicators in the national standards and to make a more comprehensive and integrated evaluation of power quality, this study proposes a power quality evaluation method based on graph convolutional network. A power quality assessment system with graded indicators is proposed according to the current national standards. The correlation between the various power quality assessment indicators is initially determined, and on this basis the indicator relationship diagram is determined, a graph neural network model is built and trained, and the error rate of the test set is 9.02%. A comparison and analysis with other assessment methods using actual measurement data of a power system proves that the proposed method is more effective in assessing power quality over a long time span.

Key words: power quality, comprehensive evaluation, graph convolutional network, indicator, correlation, graph, adjacency matrix, semi-supervised training

CLC Number:

TP183

HUANG Hongqing, NI Daohong, LIU Xuesong. Comprehensive Evaluation of Power Quality Based on Graph Convolutional Network[J].Electronic Science and Technology, 2024, 37(9): 43-47.

Figures/Tables 8

Table 1.

Figure 1.

Table 2.

Figure 2.

Figure 3.

Figure 4.

Table 3.

Table 4.

References 19

[1]	程浩忠. 电能质量概论[M]. 北京: 中国电力出版社, 2013:1-4.
	Cheng Haozhong. Introduction to power quality[M]. Beijing: China Electric Power Press, 2013:1-4.
[2]	Fuchs E, Masoum M. Power quality in power system and electrical machines[M]. New York: Academic Press, 2008:2-9.
[3]	汪飞, 全晓庆, 任林涛. 电能质量扰动检测与识别方法研究综述[J]. 中国电机工程学报, 2021, 41(12):4104-4120.
	Wang Fei, Quan Xiaoqing, Ren Lintao. Review of power quality disturbance detection and identification methods[J]. Proceedings of the CSEE, 2021, 41(12):4104-4120.
[4]	刘颖英, 冯丹丹, 林才华, 等. 电能质量综合评估研究现状及发展趋势[J]. 电力系统保护与控制, 2020, 48(4):167-176.
	Liu Yingying, Feng Dandan, Lin Caihua, et al. Current status and development trend of power quality compr-ehensive assessment[J]. Power System Protection and Control, 2020, 48(4):167-176.
[5]	Milanović J, Abdelrahman S, Liao H. Compound index for power quality evaluation and benchmarking[J]. IET Generation, Transmission and Distribution, 2018, 12(19):4269-4275.
[6]	Zeng Q, Yang H, Yang X, et al. Comprehensive evaluation of power quality considering index correlation[J]. Journal of Electric Power Systems and Automation, 2016, 28(7):73-78.
[7]	Wang Y C, Tang H, Peng D M, et al. Comprehensive evaluation method of power grid operation consideringindex correlation[C]. Taiyuan: The Fifth IEEE Conference on Energy Internet and Energy System Integration, 2021:2248-2253.
[8]	Shi H T, Li Y F, Jiang Z N, et al. Comprehensive evaluation of power quality for microgrid based on CRI-TIC method[C]. Nanjing: IEEE the Ninth International Power Electronics and Motion Control Conference, 2020:1667-1669.
[9]	魏业文, 吴希韬, 聂俊波, 等. 基于博弈论-改进TOPSIS的电能质量综合评价[J]. 电子测量技术, 2021, 44(16):50-56.
	Wei Yewen, Wu Xitao, Nie Junbo, et al. Comprehensive evaluation of power quality based on BWM-CRITIC-TOPSIS method[J]. Electronic Measurement Technology, 2021, 44(16):50-56.
[10]	Shi H T, Su G, Li Y F, et al. A comprehensive power quality assessment method based on logarithmic correction for microgrid[C]. Chengdu: The Fourth Asia Energy and Electrical Engineering Symposium, 2022:97-101.
[11]	刘康康, 杨超, 薛仰孝. 基于FAHP组合赋权TOPSIS的电能质量综合评估[J]. 智能计算机与应用, 2021, 11(9):103-107.
	Liu Kangkang, Yang Chao, Xue Yangxiao. Comprehensive evaluation of TOPSIS power quality based on FAHP combination weighting[J]. Intelligent Computer and Applications, 2021, 11(9):103-107.
[12]	国家技术监督局. GB/T 12325-2008,电能质量供电电压偏差[S]. 北京: 中国标准出版社, 2008.
	The State Bureau of Quality and Technical Supervision. GB/T 12325-2008,Power quality-deviation of supply voltage[S]. Beijing: Standards Press of China, 2008.
[13]	国家技术监督局. GB/T 15945-2008,电能质量电力系统频率偏差[S]. 北京: 中国标准出版社, 2008.
	The State Bureau of Quality and Technical Supervision. GB/T 15945-2008,Power quality-frequency deviation for power system[S]. Beijing: Standards Press of China, 2008.
[14]	国家技术监督局. GB/T 30137-2013,电压暂降与短时中断[S]. 北京: 中国标准出版社, 2013.
	The State Bureau of Quality and Technical Supervision. GB/T 30137-2013,Power quality-voltage dips and short interruptions[S]Beijing:Standards Press of China, 2013..
[15]	国家技术监督局. GB/T 15543-2008,电能质量三相电压不平衡度[S]. 北京: 中国标准出版社, 2008.
	The State Bureau of Quality and Technical Supervision. GB/T 15543-2008,Power quality-three-phrase voltage unbalence[S]. Beijing: Standards Press of China, 2008.
[16]	国家技术监督局. GB/T 12326-2008,电能质量电压波动和闪变[S]. 北京: 中国标准出版社, 2008.
	The State Bureau of Quality and Technical Supervision. GB/T 12326-2008,Power quality-voltage fluctuation and flicker[S]. Beijing: Standards Press of China, 2008.
[17]	国家技术监督局. GB/T 14549-1993,电能质量公用电网谐波[S]. 北京: 中国标准出版社, 1993.
	The State Bureau of Quality and Technical Supervision. GB/T 14549-1993,Quality of electric energy supply harmonics in public supply network[S]. Beijing: Standards Press of China, 1993.
[18]	Kipf T, Welling M. Semi-supervised classification with graph convolutional networks[C]. Toulon: Proceedings of the Fifth International Conference on Learning Representations, 2017:1-14.
[19]	徐冰冰, 岑科廷, 黄俊杰, 等. 图卷积神经网络综述[J]. 计算机学报, 2020, 43(5):755-780.
	Xu Bingbing, Cen Keting, Huang Junjie, et al. A survey on graph convolutional neural network[J]. Chinese Journal of Computers, 2020, 43(5):755-780.

国家标准编号	国家标准名称
GB/T 12325-2008	电能质量供电电压偏差^[12]
GB/T 15945-2008	电能质量电力系统频率偏差^[13]
GB/T 30137-2013	电压暂降与短时中断^[14]
GB/T 15543-2008	电能质量三相电压不平衡^[15]
GB/T 12326-2008	电能质量电压波动和闪变^[16]
GB/T 14549-1993	电能质量公用电网谐波^[17]

电能质量指标	限值
电压偏差/%	7.0
闪变/%	1.0
频率偏差/Hz	0.2
三相不平衡度/%	2.0
总谐波畸变/%	5.0
奇次谐波电压含有率/%	4.0

编号		1	2	3	4	5	6
⑤	avg	0.301 3	0.505 1	0.569 0	0.241 9	0.641 0	0.427 0
⑤	max	0.338 2	0.585 4	0.619 8	0.985 7	0.861 8	0.856 1
⑥	avg	0.060 0	0.040 0	0.180 0	0.102 8	0.169 4	0.055 9
⑥	max	0.070 0	0.040 0	0.180 0	4.590 0	0.225 0	0.210 0
⑦	avg	0.050 0	0.040 0	0.180 0	0.190 0	0.200 0	0.060 8
⑦	max	0.060 0	0.040 0	0.220 0	2.001 0	0.250 0	0.120 0
⑧	avg	0.032 8	0.138 2	0.303 7	0.017 1	0.559 2	0.037 3
⑧	max	0.165 0	0.167 9	0.345 0	0.231 3	5.018 4	0.244 0
⑨	avg	0.100 0	0.135 0	0.110 0	0.128 9	0.262 5	0.109 8
⑨	max	0.110 0	0.150 0	0.150 0	0.630 0	0.800 0	0.240 0
⑩	avg	0.524 0	0.496 0	1.140 0	0.603 8	1.467 8	0.513 1
⑩	max	0.598 0	0.558 0	1.220 0	0.866 0	1.670 0	0.948 0

数据编号	文献[8]	文献[10]	文献[11]	本文
1	1.342 0	1.163 1	1.214 8	1.287 6
2	1.939 4	1.545 0	1.619 9	1.907 1
3	2.897 7	2.859 3	2.876 5	2.887 5
4	1.275 6	1.212 1	1.335 4	1.628 1
5	3.747 1	4.022 4	3.632 6	4.468 6
6	1.647 1	1.276 2	1.427 4	2.307 6

Comprehensive Evaluation of Power Quality Based on Graph Convolutional Network

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 19

Related Articles 15

Metrics

Comments

Recommended 0

[1]	YANG Xiao, LI Gaolei. Persistent Clean-Label Backdoor Attack for Semi-Supervised Graph Node Classification [J]. Electronic Science and Technology, 2024, 37(9): 57-63.
[2]	WAN Shengrui, ZHOU Zhifeng, WU Minghui, WANG Liduan, ZHOU Wei. Coarse Registration Method of Double-Layer Sampling Point Cloud for Shield Topography Reconstruction [J]. Electronic Science and Technology, 2024, 37(7): 60-65.
[3]	LI Jing, PENG Tao. Date Communication over Mobile Voice Channel Based on Pitch Delay Steganography [J]. Electronic Science and Technology, 2024, 37(7): 66-71.
[4]	FANG Yingli, FANG Yuming. Research on AES and ECC Algorithm Image Encryption Based on FPGA [J]. Electronic Science and Technology, 2024, 37(6): 92-97.
[5]	ZHU Jinkai, FANG Lanting, JI Xiaowen, HUANG Jie. Multimodal Android Malware Detection Method Based on Behavioral and Semantic Characteristics [J]. Electronic Science and Technology, 2024, 37(5): 71-78.
[6]	HAN Xinsheng, KAN Jiarong, LING Huiying, WANG Peng, CHENG Qian. Research on Double Layer Ring Equalizer for Lithium Battery Pack of Electric Vehicle [J]. Electronic Science and Technology, 2024, 37(4): 16-24.
[7]	WU Jiacheng, YU Xiao. A Review of Research on Cybersecurity Risk Assessment Methods [J]. Electronic Science and Technology, 2024, 37(3): 10-17.
[8]	ZHANG Haifang,HE Qinglong,ZHANG Lin. Power Load Forecasting Model Based on Expansion Period [J]. Electronic Science and Technology, 2024, 37(2): 1-5.
[9]	NAN Jiao,SUN Zhanquan. Sorting Method of Multi Leads ECG Based on Mutual Information [J]. Electronic Science and Technology, 2024, 37(2): 55-60.
[10]	LI Yudong,MA Jinquan,XIE Zongfu,SHEN Xiaolong. Research on Task Scheduling of Heterogeneous Platform Signal Processing [J]. Electronic Science and Technology, 2024, 37(1): 24-32.
[11]	ZHANG Dagui,ZHOU Zhifeng,ZHANG Yi,WANG Liduan. TDOA Sound Source Localization Method Based on Particle Swarm Optimization Algorithm [J]. Electronic Science and Technology, 2023, 36(9): 21-28.
[12]	LIN Zhipeng,SUN Xiaohui,WEN Chenglin. An Extended Dimension Kalman Filter Method Based on Additive Hidden Variables [J]. Electronic Science and Technology, 2023, 36(5): 47-54.
[13]	SUN Xiuting,LI Yonggang,ZHANG Xing,XU Zhongyu,YU Yu. Importance Evaluation of Power Grid Nodes Based on Dynamic and Static Indexes [J]. Electronic Science and Technology, 2023, 36(4): 1-8.
[14]	TANG Zenan,MIAO Xiaodan,YANG Jian,YUAN Tianchen. Research on Low Mach Number Transitional Cavity Jet Noise Reduction of High-Speed Train Pantograph [J]. Electronic Science and Technology, 2023, 36(4): 29-35.
[15]	ZUO Bin,LI Feifei. An Effective Segmentation Method for COVID-19 CT Image Based on Attention Mechanism and Inf-Net [J]. Electronic Science and Technology, 2023, 36(2): 22-28.