基于小波-树突状网络的配电网单相接地故障辨识方法

A Wavelet Transform Dendritic Network-Based Single-Phase Ground Fault Identification Method

单相接地故障是智能配电网中最为常见的一种故障,但当接地过渡电阻较高时单相接地故障稳态特征不明显,难以与正常扰动进行区分,故障辨识的准确率受到较大影响。针对上述情况,文章采用小波分析法对故障信号进行三级分解,得到不同的高频成分,构建特征向量,并采用树突状神经网络对故障特征向量进行分类和识别。在MATLAB/Simulink中建立仿真模型,测试结果表明,运用该方法能够对单相接地高阻故障和正常扰动进行准确快速分类,其收敛速度和分类准确率均优于一般的智能算法。同时在信号存在噪声的情况下,该方法仍然具有较高的准确率。

Ground transition resistance is high,the steady-state characteristics of single-phase ground fault are not obvious,and it is difficult to distinguish it from normal disturbance,and the accuracy of fault identification is greatly affected.To address the above problem,wavelet analysis is used to decompose the fault signal at three levels to obtain different high-frequency components to construct the feature vectors,and dendritic neural networks are used to classify and identify the fault feature vectors.The simulation model is established in MATLAB/Simulink,and the test results show that the method can accurately and quickly classify single-phase grounding high-resistance faults and normal disturbances,and its convergence speed and classification accuracy are better than those of general intelligent algorithms.At the same time,this method still has a high accuracy when the signal is noisy.