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引用次数: 4
摘要
本文提出了一种配电系统故障检测与分类的方法。使用Digsilent Power Factory软件对66千伏电力系统的一个部分进行建模。故障事件在模型上实例化。将故障事件信号作为离散小波变换的输入,得到故障特征,然后将这些特征作为支持向量机(SVM)和人工神经网络(ANN)的输入,进行故障分类和检测。此外,采用高斯过程回归(GPR)技术对配电线路沿线的故障位置进行估计。在MATLAB中开发了故障检测、分类和定位估计方案。结果表明,该方法能以较好的准确率和最小的故障估计误差对配电网中的大多数故障进行分类。该方法在实际电力系统中得到了进一步验证。提出了一种用于配电网故障定位检测、分类和估计的混合方法。
A Support Vector Machine Based Fault Diagnostic Technique In Power Distribution Networks
In this paper, a method for detection and classification of faults in an electrical power distribution system is presented. Digsilent Power Factory software was used to model a section of a 66 kV power system. Fault incidents were instantiated on the model. The signal obtained from fault incidences were subsequently fed as input to discrete wavlet transform in order to obtained fault features and subsequently the features were then used as inputs for a support vector machine (SVM) and artificial neural network (ANN) for fault classification and detection. In addition, a Gaussian Process Regression (GPR) technique was employed for estimation of fault locations along the distribution line. Fault detection, classification and location estimation scheme were developed in MATLAB. The method showed that most faults on electric power distribution network can be classified with a good accuracy and minimum fault estimation error. The method is further validated on a real world power system. A hybrid method is thus proposed for detection, classification and estimation of fault location in a distribution network.