New Approach for Fault Identification and Classification in Microgrids

Abstract

Power system fault identification and classification continues to be one of the most important challenges faced by the power system operators. In spite of the dramatic improvements in this field, the existing protection devices are not able to successfully identify and classify all types of faults which occur power system. The situation in even more the complex in the case of microgrids due to their dynamic behavior and inherent peculiarities. This paper presents a novel method for identification and classification of faults in the microgrid. The proposed method is based on Descrete Wavelet Transform (DWT) and Artificial Neural Networks (ANN). The model is completely developed in MATLAB Simulink and is significant because it can be applied for practical identification and classification of faults in microgrids. The obtained results indicate that the proposed algorithm can be used as a promising foundation for the future implementation of the microgrid protection devices.