摘要
当辐射状配电网不同分支发生故障时,其故障电压行波经由不同分支组合的传播路径到达母线侧量测端,由量测端获得的故障暂态电压的自然频率及其幅值分布亦不相同。不同分支组合的行波传播路径与自然频率及其幅值分布之间存在着映射关系。可利用人工神经网络(ANN)强大的非线性拟合能力来反映此种映射关系,实现辐射状配电网的故障定位及分支识别。利用故障后四分之一工频周期时窗的零序电压自然频率作为分层分布式ANN模型的输入样本,先进行故障定位;再以自然频率对应的幅值作为输入样本,进行故障分支识别,故障距离和故障点所在分支编号作为其输出。大量电磁暂态仿真表明,该方法有效。
When fault occurs in different branches of radial distribution networks,voltage traveling waves will get to the measuring point installed on the bus side along paths of different branch combinations,leading to differences in natural frequency and its amplitude distribution of the transient voltage obtained from the measuring end.As there is a mapping relationship between the distribution and amplitudes of the natural frequencies and the propagation paths,we can use the strong nonlinear fitting capability of artificial neural networks （ANN） to realize the fault location and branch identification.By using the natural frequency of zero-sequence voltage data abstracted from a quarter of the industrial cycle after fault occurs as input samples of the layered and distributed ANN model to achieve fault location firstly.Employ the amplitudes corresponding to the natural frequencies as input samples to realize branch identification,outputting the fault distance and branch number of the failure point.Large numbers of electromagnetic transient simulations show that the method presented is effective.
出处
《电力系统自动化》
EI
CSCD
北大核心
2014年第5期83-89,共7页
Automation of Electric Power Systems
基金
国家高技术研究发展计划(863计划)资助项目(2012AA050213)
国家自然科学基金资助项目(50977039
51267009
U1202233)
云南省科技攻关项目(2011BA004)
云南省重点项目(2011FA032)
高等学校博士学科点专项科研基金资助项目(20105314110001)~~
关键词
辐射状配电网
故障定位
自然频率
分层分布式人工神经网络
radial distribution network
fault location
natural frequency
layered and distributed artificial neural networks
