月刊
ISSN 1000-7229
CN 11-2583/TM
电力建设 ›› 2023, Vol. 44 ›› Issue (3): 77-84.doi: 10.12204/j.issn.1000-7229.2023.03.008
• 新型电力系统下配电网规划与运行优化关键技术研究及应用·栏目主持 王守相教授、赵倩宇博士· • 上一篇 下一篇
收稿日期:
2022-04-18
出版日期:
2023-03-01
发布日期:
2023-03-02
通讯作者:
张文海(1989),男,博士,助理研究员,高级工程师,主要研究方向为配网故障诊断与电力扰动分析学,E-mail:作者简介:
谭媛(1994),女,硕士,工程师,主要研究方向为配网故障原因识别,E-mail:405402585@qq.com基金资助:
TAN Yuan(), ZHANG Wenhai(
), WANG Yang(
)
Received:
2022-04-18
Online:
2023-03-01
Published:
2023-03-02
Supported by:
摘要:
配网故障原因的准确识别对于缩短故障查找时间、提高供电恢复速度和供电可靠性有重要意义。根据责任归属可将配网故障原因分为内部原因和外部原因,内部原因指设备绝缘弱化、过电压等电气相关原因,而外部故障通常由于天气、动物或人类活动引起。由于外部原因导致的故障是多种因素共同作用的结果,为此提出融合线路参数、天气、时间等非电气量信息及动作电流、故障相数等电气量信息的故障外部原因识别方法,首先对5种典型外部故障原因的特点及相关影响因素进行分析,构建识别模型,然后使用无监督学习训练得到深度信念网络各层的最优参数,利用有监督学习对全局参数进行微调,得到基于深度信念网络的配网故障外部原因识别模型,最后利用西部某地区的实际故障数据对算法的准确性进行了验证,结果显示识别准确率可达94.82%,证明了方法的正确性。
中图分类号:
谭媛, 张文海, 王杨. 基于多源信息融合的配网故障外部原因识别[J]. 电力建设, 2023, 44(3): 77-84.
TAN Yuan, ZHANG Wenhai, WANG Yang. Distribution System External Fault Causes Identification based on Multi-Source Information Fusion[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(3): 77-84.
[1] | 王玲, 邓志, 马明, 等. 基于状态估计残差比较的配电网故障区段定位方法[J]. 电力系统保护与控制, 2021, 49(14): 132-139. |
WANG Ling, DENG Zhi, MA Ming, et al. A method for locating fault sections in distribution networks based on the comparison of state estimation residual errors[J]. Power System Protection and Control, 2021, 49(14): 132-139. | |
[2] |
白星振, 宋昭杉, 葛磊蛟, 等. 含分布式电源的复杂配电网相间故障定位等效解耦模型[J]. 电力建设, 2022, 43(2): 45-53.
doi: 10.12204/j.issn.1000-7229.2022.02.006 |
BAI Xingzhen, SONG Zhaoshan, GE Leijiao, et al. An equivalent decoupling model for fault location in complex distribution network with distributed generation[J]. Electric Power Construction, 2022, 43(2): 45-53.
doi: 10.12204/j.issn.1000-7229.2022.02.006 |
|
[3] |
刘健, 张志华, 黄炜, 等. 分布式电源接入对配电网故障定位及电压质量的影响分析[J]. 电力建设, 2015, 36(1): 115-121.
doi: 10.3969/j.issn.1000-7229.2015.01.018 |
LIU Jian, ZHANG Zhihua, HUANG Wei, et al. Influence of distributed generation on fault location and voltage quality of distribution network[J]. Electric Power Construction, 2015, 36(1): 115-121.
doi: 10.3969/j.issn.1000-7229.2015.01.018 |
|
[4] |
雷倩, 吉兴全, 文福拴, 等. 利用暂态分量的含分布式电源配电系统故障诊断[J]. 电力建设, 2016, 37(2): 42-49.
doi: 10.3969/j.issn.1000-7229.2016.02.006 |
LEI Qian, JI Xingquan, WEN Fushuan, et al. Fault diagnosis of distribution system with distributed generation employing transient component[J]. Electric Power Construction, 2016, 37(2): 42-49.
doi: 10.3969/j.issn.1000-7229.2016.02.006 |
|
[5] |
XU L, CHOW M Y. A classification approach for power distribution systems fault cause identification[J]. IEEE Transactions on Power Systems, 2006, 21(1): 53-60.
doi: 10.1109/TPWRS.2005.861981 URL |
[6] |
MINNAAR U J, NICOLLS F, GAUNT C T. Automating transmission-line fault root cause analysis[J]. IEEE Transactions on Power Delivery, 2016, 31(4): 1692-1700.
doi: 10.1109/TPWRD.2015.2503478 URL |
[7] | BARRERA NÚÑEZ V, KULKARNI S, SANTOSO S, et al. Feature analysis and classification methodology for overhead distribution fault events[C]// IEEE PES General Meeting. July 25-29, 2010, Minneapolis, MN, USA. IEEE, 2010: 1-8. |
[8] | 秦雪, 刘亚东, 孙鹏, 等. 基于故障波形时频特征配网故障识别方法研究[J]. 仪器仪表学报, 2017, 38(1): 41-49. |
QIN Xue, LIU Yadong, SUN Peng, et al. Study on the line fault root-cause identification method in distribution networks based on time-frequency characteristics of fault waveforms[J]. Chinese Journal of Scientific Instrument, 2017, 38(1): 41-49. | |
[9] | KULKARNI S, LEE D, ALLEN A J, et al. Waveform characterization of animal contact, tree contact, and lightning induced faults[C]// IEEE PES General Meeting. July 25-29, 2010, Minneapolis, MN, USA. IEEE, 2010: 1-7. |
[10] | XU L, CHOW M Y, TAYLOR L S. Data mining and analysis of tree-caused faults in power distribution systems[C]// 2006 IEEE PES Power Systems Conference and Exposition. October 29 - November 1, 2006, Atlanta, GA, USA. IEEE, 2007: 1221-1227. |
[11] |
CHOW M Y, TAYLOR L S. Analysis and prevention of animal-caused faults in power distribution systems[J]. IEEE Transactions on Power Delivery, 1995, 10(2): 995-1001.
doi: 10.1109/61.400829 URL |
[12] |
DOOSTAN M, CHOWDHURY B H. Power distribution system fault cause analysis by using association rule mining[J]. Electric Power Systems Research, 2017, 152: 140-147.
doi: 10.1016/j.epsr.2017.07.005 URL |
[13] | XU L, CHOW M Y, TIMMIS J. Power Distribution Outage Cause Identification using Fuzzy Artificial Immune Recognition Systems (FAIRS) algorithm[C]// 2007 IEEE Power Engineering Society General Meeting. June 24-28, 2007, Tampa, FL, USA. IEEE, 2007: 1-8. |
[14] |
CHOW M Y, YEE S O, TAYLOR L S. Recognizing animal-caused faults in power distribution systems using artificial neural networks[J]. IEEE Transactions on Power Delivery, 1993, 8(3): 1268-1274.
doi: 10.1109/61.252652 URL |
[15] | LI L N, CHE R F, ZANG H Z. A fault cause identification methodology for transmission lines based on support vector machines[C]// 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). October 25-28, 2016, Xi’an. IEEE, 2016: 1430-1434. |
[16] | 贾京龙, 余涛, 吴子杰, 等. 基于卷积神经网络的变压器故障诊断方法[J]. 电测与仪表, 2017, 54(13): 62-67. |
JIA Jinglong, YU Tao, WU Zijie, et al. Fault diagnosis method of transformer based on convolutional neural network[J]. Electrical Measurement & Instrumentation, 2017, 54(13): 62-67. | |
[17] | 王功明, 乔俊飞, 关丽娜, 等. 深度信念网络研究现状与展望[J]. 自动化学报, 2021, 47(1): 35-49. |
WANG Gongming, QIAO Junfei, GUAN Lina, et al. Review and prospect on deep belief network[J]. Acta Automatica Sinica, 2021, 47(1): 35-49. | |
[18] | KARTHIGA R, USHA G, RAJU N, et al. Transfer learning based breast cancer classification using one-hot encoding technique[C]// 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). March 25-27, 2021, Coimbatore, India. IEEE, 2021: 115-120. |
[19] |
石鑫, 朱永利. 深度学习神经网络在电力变压器故障诊断中的应用[J]. 电力建设, 2015, 36(12): 116-122.
doi: 10.3969/j.issn.1000-7229.2015.12.018 |
SHI Xin, ZHU Yongli. Application of deep learning neural network in fault diagnosis of power transformer[J]. Electric Power Construction, 2015, 36(12): 116-122.
doi: 10.3969/j.issn.1000-7229.2015.12.018 |
|
[20] | DENIL M, SHAKIBI B, DINH L, et al. Predicting parameters in deep learning[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. December 5 - 10, 2013, Lake Tahoe, Nevada. New York: ACM, 2013: 2148-2156. |
[21] | 杨杰, 吴浩, 董星星, 等. 基于电流故障分量特征和随机森林的输电线路故障类型识别[J]. 电力系统保护与控制, 2021, 49(13): 53-63. |
YANG Jie, WU Hao, DONG Xingxing, et al. Transmission line fault type identification based on the characteristics of current fault components and random forest[J]. Power System Protection and Control, 2021, 49(13): 53-63. | |
[22] | 申元, 马仪, 孟见刚, 等. 基于BP神经网络的输电线路故障原因辨识研究[J]. 智能电网, 2017, 5(2): 134-141. |
SHEN Yuan, MA Yi, MENG Jiangang, et al. Research on transmission line fault reason recognition based on BP neural network[J]. Smart Grid, 2017, 5(2): 134-141. | |
[23] | XU L, CHOW M C, GAO X Z. Comparisons of logistic regression and artificial neural network on power distribution systems fault cause identification[C]// Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05. June 28-30, 2005, Espoo, Finland. IEEE, 2005: 128-131. |
[24] | 李宇, 杨柳林. 基于卷积神经网络的配电网单相接地故障识别[J]. 电气工程学报, 2020, 15(3): 22-30. |
LI Yu, YANG Liulin. Identification of single-phase-to-earth fault in distribution network based on convolutional neural network[J]. Journal of Electrical Engineering, 2020, 15(3): 22-30. |
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