A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine

Abstract

A new protection scheme based on applying a combination of wavelet multi-resolution singular spectrum entropy and support vector machine is proposed to identify different types of grid faults in a three-phase grid-tied photovoltaic system. In this technique, discrete wavelet transform with multi-resolution singular spectrum entropy is utilized to extract the unique features of three-phase voltage signals at the point of common coupling. The three-phase voltage signals are decomposed to provide detail and approximation coefficients of wavelet transform. Then, various features between different types of grid faults can be extracted by a combination of multi resolution analysis and spectrum analysis with entropy as the output. The constructed features vector is utilized as input data of a support vector machine classifier to identify and classify various types of faults. The results illustrate that the proposed intelligent technique not only recognizes different types of grid faults correctly, but also performs quickly in identifying grid faults in a grid-connected photovoltaic system. Apart from this, a graphical investigation is executed to observe the effects of different types of grid faults in photovoltaic (PV) operation which highlight the necessity of intelligent protection methods to protect PV systems.