Fault Detection in Power System Integrated Network with Distribution Generators Using Machine Learning Algorithms

The generation of electricity from renewable energy sources (RES) is becoming more popular globally. This is because primary sources of electricity such as coal have a negative environmental impact. The introduction of RES into the existing power distribution grid has brought technical challenges. Fault detection with high reliability in power distribution network integrated with RES is one of the major challenges. In this paper, we propose a technique for fault detection in an integrated network. A reduced 22kV integrated power system is modelled in Digsilent Power Factory. Various fault current signals are generated from the model. Discrete wavelet transform (DWT) is used to extract statistical features from the fault current obtained through the study of the model. Subsequently, the extracted features are fed into the support vector machine scheme for fault detection and classification. In this paper, we also tested the performance of neural network (NN) and decision tree (DT) classifiers. A combined technique comprising of DWT and SVM is proposed. The proposed method is tested using a machine learning platform WEKA. The proposed method showed impressive classification results.