• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

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

基于多源信息融合的配网故障外部原因识别

谭媛(), 张文海(), 王杨()   

  1. 四川大学电气工程学院, 成都市 610065
  • 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:zhangwh2079@scu.edu.cn
  • 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
    王杨(1991),男,博士,研究员,四川省人才计划入选专家,主要研究方向围绕新型电力系统“高比例新能源、高比例电力电子”接入这一特征,开展新型电力系统电能质量分析与控制、宽频振荡广域监测、溯源与抑制、非线性控制理论在新型电力系统中的应用等,E-mail:fwang@scu.edu.cn
  • 基金资助:
    国家重点研发计划项目(2020YFF0305800);四川省自然科学基金(2022NSFSC0234)

Distribution System External Fault Causes Identification based on Multi-Source Information Fusion

TAN Yuan(), ZHANG Wenhai(), WANG Yang()   

  1. College of Electrical Engineering, Sichuan University, Chengdu 610065, China
  • Received:2022-04-18 Online:2023-03-01 Published:2023-03-02
  • Supported by:
    National Key R&D Program of China(2020YFF0305800);Sichuan Natural Science Foundation(2022NSFSC0234)

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。

关键词: 配网故障, 外部原因, 原因识别, 深度学习, 多源信息融合

Abstract:

Accurate identification of distribution network fault causes is of great significance to shorten the time of fault search and improve the speed of power supply recovery and power supply reliability. According to the attribution of responsibility, the causes of distribution network fault can be divided into internal causes and external causes. Internal causes refer to electrical related causes such as equipment insulation weakening and overvoltage, while external faults usually refer to weather, animal or human activities. Faults caused by external causes are the result of a combination of many factors. In this paper, a fault external cause identification method is proposed, which integrates the non-electrical information such as line parameters, weather and time, and the electrical information such as fault current and phase number. Firstly, the characteristics and related influencing factors of five typical external fault causes are analyzed to build the basis of identification model. Then unsupervised learning training is used to obtain the optimal parameters of each layer of deep belief network, and supervised learning is used to fine-tune the global parameters, and the classification model of external causes of distribution network faults based on deep belief network is obtained. Finally, the accuracy of the algorithm is verified by using the actual fault data of a certain area in western China. The result shows that the recognition accuracy can reach 94.82%, which proves the correctness of the method.

Key words: distribution system fault, external fault causes, fault cause identification, deep learning, multi-source information fusion

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