摘要
根据故障行波的传输特性和折、反射机理,提出了一种基于单端行波信号的输电线路区内外故障智能判断新方法。该方法通过门控循环单元搭建了一个深度学习分类网络,根据线路长度将单端故障行波信号切割为短序列作为神经网络的输入,输出为反向区外故障、区内故障、正向区外故障三种故障位置的概率,并采用自适应Adam优化算法训练神经网络的参数。算例结果表明,所提方案充分有效地利用行波信号中的故障特征,能准确辨别输电线路区内外故障,在线路的首末端均具有较高的鲁棒性,且在高噪声干扰的情况下仍能保持良好的准确率。
According to the transmission characteristics and the refraction and reflection principle of the traveling wave, a novel intelligent method for internal and external fault diagnosis of transmission line was proposed based on single-ended traveling wave signal. The deep classification neural network was constructed by gated recurrent unit. The network input was set as short sequences, which is obtained by dividing the single-ended fault traveling wave signal according to the line length. The network output was the probability of three type of fault locations(reverse external fault, internal fault and forward external fault). The adaptive Adam algorithm was used to train the parameters of the neural network training. The results of case studies show that the proposed method can accurately distinguish the internal and external faults of the transmission line, which has highly robust at the beginning and end of the line and strong ability of anti-noise.
作者
吕东晓
凌谢津
王英英
陈祥文
任康杰
李银红
LV Dong-xiao;LING Xie-jin;WANG Ying-ying;CHEN Xiang-wen;REN Kang-jie;LI Yin-hong(Central China Branch of State Grid Corporation of China,Wuhan 430077,China;State Key Laboratory of Advanced Electromagnetic Engineering and Technology,School of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《水电能源科学》
北大核心
2022年第6期207-210,206,共5页
Water Resources and Power
基金
国家重点研发计划(2016YFB0900100)。
关键词
区内外故障判断
故障行波
深度学习
循环神经网络
internal and external fault diagnosis
travelling wave
deep learning
recurrent neural network
