基于CNN-SVM的配电网故障分类研究

Fault Classification in Distribution Network Based on CNN-SVM

针对CNN在配电网高阻故障时分类准确率低的问题,提出了一种将CNN和SVM相结合的配电网故障分类研究方法。首先将故障数据转换为时频谱灰度图,作为训练集输入到CNN中;然后采用SVM代替CNN中的Softmax分类器构建CNN-SVM模型,并通过网格搜索算法对SVM超参数进行寻优;最后进行多工况算例分析验证所提方法优越性。算例分析结果表明,CNN-SVM模型比传统CNN-Softmax模型在高阻故障时具有更高的分类准确率,且在主变压器中性点接地方式变化、网络结构变化、噪声干扰及单相弧光接地等工况下仍具有良好的适应性。

Targeting the low classification accuracy of convolutional neural networks(CNN)during high impedance fault in distribution network,the paper proposes a fault classification method for the distribution network by combining CNN and SVM.Firstly,the fault data is converted into a time-frequency spectrum grayscale image and is entered into the CNN as a training set.Then support vector machine(SVM)instead of softmax classifier in the CNN is used to build the CNN-SVM model,and the hyper-parameters of the SVM are optimized by grid search algorithm.Finally,a variety of numerical examples are used to verify the superiority of the proposed method.The results of the example analysis show that the CNN-SVM model has higher classification accuracy than the traditional CNNSoftmax model in case of the high impedance fault,and has better adaptability under the conditions of changing the neutral grounding mode of main transformer,network structure change,noise interference and single-phase arc grounding.