Fault Pattern Recognition in Power Distribution Integrated Network with Renewable Energy Source

Abstract

The challenge with most developing countries is to maintain a reliable and sustainable electricity supply. This has a depleting effect on the economic development of most states. In order to reduce the impact of energy shortages, there has been an extensive attempt to use renewable energy sources to generate electricity. There are however technical challenges of integrating the existing power system grid with the renewable external sources. These challenges include adequate power system protection, energy security, and reliability of external sources. In this paper, we investigate the fault pattern recognition and detection in a power distribution grid integrated with the wind energy source. A reduced Eskom 22kV and wind power energy source integrated is modeled using MATLAB/Simulink. From the integrated model, various types of power systems faults are generated. We further investigated the use of local polynomial approximation (LPA) for signal decomposition and support vector machine (SVM) for fault classification and detection. We also tested the performance of the naive Bayes classifier. In this paper, a hybrid technique based on LPA and SVM is proposed for fault pattern recognition and detection in a power distribution integrated system with the wind energy source. The proposed method was further tested using machine learning platforms WEKA and Orange. The results of the classifiers gave the accuracy of between 98 and 99 %.