摘要
随着人工智能算法的快速发展,在配网过电压识别中层数较少的卷积神经网络已得到应用。深层次网络有较高的识别率,但需要大量数据样本,目前已有数据集数据量不足,不能满足深层级网络训练所需。为此提出了一种满足深层次网络训练所需的配电网过电压数据集建立方法。首先利用电磁暂态仿真软件EMTPworks仿真10 kV配网5种典型的过电压并且编辑相应的JavaScript脚本,通过改变故障初相角、过渡电阻、线路长度等参数产生16272个数据。然后将三相过电压一维信号进行连续小波变换,得到相应二维时频图,并根据原始信号的特征自动标记二维时频图,从而建立了完整的配电网过电压数据集。最后利用卷积神经网络(CNN)对5类过电压信号数据的有效性进行验证。结果表明,构造的数据集数据规模大,有效性高,能够满足深层次网络需要。
With the rapid development of artificial intelligence algorithms,convolutional neural networks with fewer layers have been used in distribution network overvoltage recognition.The deep-level network has a higher recognition rate,but requires a large number of data samples.At present,the amount of data in the existing data set is insufficient to meet the needs of deep-level network training.To this end,a method for establishing distribution network overvoltage data sets required for deep-level network training was proposed.Firstly,the electromagnetic transient simulation software EMTPworks was used to simulate 5 typical overvoltages of 10 kV distribution network and the corresponding JavaScript script was edited,and 16272 pieces of data were generated by changing the parameters of the fault initial phase angle,transition resistance,line length and other parameters.Then the three phase overvoltage one-dimensional signal was subjected to continuous wavelet transform to obtain a two dimensional time-frequency diagram of the corresponding overvoltage.Afterwards,the two-dimensional time frequency map was automatically marked according to the characteristics of the original signal,thereby a complete distribution network overvoltage data set was established.Finally,the convolutional neural network(CNN)was used to verify the validity of the 5 types of overvoltage signal data.The results show that the constructed data set has large scale and high validity,and can meet the needs of deep-level network.
作者
贾俊青
吕超
刘丁华
徐浩
JIA Junqing;LÜChao;LIU Dinghua;XU Hao(Inner Mongolia Electric Power Research Institute,Huhhot 010020,Nei Monggol,China;School of Electric Power,Inner Mongolia University of Technology,Huhhot 010321,Nei Monggol,China)
出处
《电气传动》
2022年第9期57-62,73,共7页
Electric Drive
关键词
配电网
过电压
连续小波变换
JAVASCRIPT脚本
卷积神经网络
distribution network
overvoltage
continuous wavelet transform
JavaScript script
convolutional neural network(CNN)
