Power Quality Disturbance Detection and Monitoring of Solar Integrated Micro-Grid

Abstract

Due to the popularity of microgrids and power quality disturbances (PQD) induced by renewable energies, monitoring in microgrids has risen in popularity in recent years. For monitoring the PQD, many strategies based on artificial intelligence have been proposed. However, when the electrical parameters change, the need to retrain the Artificial neural network (ANN) becomes a significant issue. This paper presents a new approach to the power quality disturbance detection and monitoring of integrated solar microgrids. The power quality event detection is accomplished by analyzing the frequency signal with Wavelet transformation (WT). The classification of power quality disturbance is achieved based on the features. For the classification of PQDs, the retrieved features are fed into a Convolutional neural network (CNN) classifier.