• 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 ›› 2018, Vol. 39 ›› Issue (10): 37-43.doi: 10.3969/j.issn.1000-7229.2018.10.005

• 现代人工智能在电力系统中的应用 栏目主持 文福拴教授、赵俊华教授、颜拥博士 • 上一篇    下一篇

灾害天气条件下电力系统故障诊断特征匹配方法

尚慧玉1,王崇宇2,陈明辉1,阳曾1,熊文1,文福拴2   

  1. 1.广州供电局有限公司,广州市 510620;2.浙江大学电气工程学院,杭州市310027
  • 出版日期:2018-10-01
  • 作者简介:尚慧玉 (1982),女,硕士,工程师,主要从事电力系统运行与控制方面的研究工作; 王崇宇 (1995),男,博士研究生,主要从事电力系统故障诊断方面的研究工作; 陈明辉 (1985),男,硕士,工程师,主要从事电力系统运行与控制方面的研究工作; 阳曾 (1981),男,硕士,高级工程师,主要从事电力系统运行与控制方面的研究工作; 熊文 (1973),男,硕士,高级工程师,主要从事电力系统运行与控制方面的研究工作; 文福拴 (1965),男,博士,教授,博士生导师,通信作者, 主要从事电力系统故障诊断与系统恢复、电力经济与电力市场、智能电网与电动汽车等方面的研究工作。
  • 基金资助:
    国家重点研发计划项目(2017YFB0902900);南方电网公司重点科技项目(GZHKJXM20160035)

A Feature Matching Based Method for Fault Diagnosis in Power Systems Under Disaster Weather

SHANG Huiyu1, WANG Chongyu2, CHEN Minghui1, YANG Zeng1, XIONG Wen1, WEN Fushuan2   

  1. 1. Guangzhou Power Supply Bureau Co., Ltd., Guangzhou 510620, China;2. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
  • Online:2018-10-01
  • Supported by:
    This work is jointly supported by National Key Research and Development Program of China (No.2017YFB0902900) and China Southern Power Grid Key Project (No. GZHKJXM20160035).

摘要: 灾害天气会导致电力通信系统的可靠性降低, 警报信息在通信过程中发生畸变的可能性上升, 进而增加对停电区域内元件准确识别故障的困难。现有研究表明, 通过对线路发生故障期间的外部环境数据进行分析可得到该线路的故障风险程度, 但此信息尚很少被用于故障诊断之中。在此背景下,提出一种利用外部天气环境信息进行故障特征匹配的电力系统故障诊断方法。首先, 利用故障期间采集得到的外部环境信息根据灰色模糊理论得到线路综合故障概率。然后, 在现有的电力系统故障诊断解析模型(优化模型)的目标函数中增加外部环境故障特征匹配指标, 对与灾害天气相关故障特征不匹配的故障假说进行惩罚。接着, 利用模拟退火遗传算法对改进的解析模型进行求解。最后, 针对广州市局部电网的故障案例进行仿真测试,结果表明所提的改进故障诊断方法对通信故障的容错性强;此外,还比较分析了考虑与不考虑外部天气环境影响时的诊断结果及差异原因。

关键词: 电力系统, 故障诊断, 灾害天气, 特征匹配, 解析模型

Abstract: Reliability of the communication system in a power system decreases under disaster weather, and then the possibility of communication distortions of alarm messages rises. As a result, it will become more difficult to accurately identify faults in the outage area(s). The existing research has demonstrated that the fault risk degree of a transmission line can be obtained by analyzing the external environmental data during the line fault. However, this information source is rarely used in existing fault diagnosis methods. Given this background, a power system fault diagnosis method based on fault feature matching using external weather environment information is proposed. First, the fault probability of a transmission line is attained by employing the grey fuzzy theory on the basis of the external weather information collected during a fault. Then, the matching index of the fault feature related to the external weather condition is added to the objective function of the existing analytical model (optimization model) for fault diagnosis, and the fault hypotheses which do not match the corresponding fault features will be punished. Next, the simulated annealing genetic algorithm is used to solve the improved analytical model. Finally, the proposed method is demonstrated by employing fault scenarios in a regional power system in Guangzhou. Simulation results show that the improved fault diagnosis method is more fault-tolerant to communication failures. In addition, the fault diagnosis outcomes with and without employing external weather information are compared and the reasons for their differences analyzed.

Key words: power system, fault diagnosis, disaster weather, feature matching, analytical model

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