基于经验模态分解与长短时记忆网络的配电网故障原因识别方法
摘要：针对识别配电网故障原因，目前的“人工巡线”方法，不仅耗费大量的人力物力资源，而且延长了停电时间。因此，提出一种基于数据驱动的配电网故障原因识别方法。首先通过对大量现场记录的故障波形数据进行分析，得到不同原因故障的机理以及波形特征，提出一种基于经验模态分解 (empirical mode decomposition，EMD) 和主成分分析(principal component analysis，PCA) 的故障特征提取方法。通过EMD将故障时域波形按照不同的时间尺度进行分解，得到具有信号局部特征的本征模态函数(intrinsic mode function，IMF) 分量。其次利用PCA对多个IMF分量进行降维，提取IMF序列中的主要特征分量并将其组成特征向量。最后提出一种基于长短期记忆网络的故障原因分类模型，用于提取特征序列的动态时间尺度特征并实现故障原因的分类。使用实际现场数据的实验结果表明，该故障原因分类模型具有较高的准确度。
Abstract:In order to identify fault causes of distribution networks, currently used artificial patrol inspection not only consumes a lot of manpower and material but also prolongs the power outage time. For this reason, a data driven based fault cause identifying for distribution network was proposed. Firstly, by means of analyzing a lot of spot-recorded fault waveform data the mechanism of different fault causes and the wave characteristics were obtained, and a fault feature extraction method based on empirical mode decomposition (abbr. EMD) and principal component analysis (abbr. PCA) was proposed. Secondly, through EMD the time domain waveform of the fault was decomposed according to different time scales to obtain intrinsic mode function (abbr. IMF) components possessing local features of the signal. Thirdly, by use of PCA the dimensionality reduction of multi-IMF components were conducted and the principal characteristic components in IMF series were extracted to compose them into eigenvectors. Finally, a fault cause classification model based on long-short term memory (abbr. LSTM) network was put forward to extract dynamic time-scale feature and to realize the classification of fault causes. The experiment results, which utilizes practical field data, show that the proposed fault cause classification model possesses a higher accuracy.
标题：基于经验模态分解与长短时记忆网络的配电网故障原因识别方法
英文标题：Fault Cause Identification Method of Distribution Network Based on Empirical Mode Decomposition and Long-Short Term Memory Network
作者：于希娟, 李欣, 宣振文, 刘硕, 刘灏,
关键词：故障原因识别, 配电网, LSTM网络, EMD分解, 主成分分析, 数据驱动,
发表日期：2023-08-10
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