A new deep learning method for the classification of power quality disturbances in hybrid power system

With the advancement of technology, the demand for high quality and sustainable electrical energy has been increased due to the widespread use of electrical devices in our daily lives. The issue of power quality in the power system is of great importance for the smooth and long-lasting operation of the electrical devices. Besides, large penetration of the hybrid power system (HPS) into the existing power grid injects the inevitable issues related to the power quality. Therefore, it is very important to detect and eliminate the power quality disturbances (PQDs) in order to obtain quality power. This paper presents a new approach deep learning-based system that can detect PQDs in the HPS. A new feature extraction approach is used to obtain the optimum Stockwell Transform (ST) contour image by applying the ST to a PQD signal. The resulting image files are given to the convolutional neural network (CNN) algorithm. Besides, optimum hyperparameters of CNN are determined by using Bayesian optimization algorithm (BOA). Thus, a recognition approach that both effectively extracts the features of PQDs and has high classification performance is proposed in this paper. The proposed recognition system is named as ST and Bayesian optimization-based CNN (STBOACNN). In order to test the performance of the proposed STBOACNN approach, PQD data obtained from the HPS with converter-based distributed generations are used. The experimental results showed that the STBOACNN is a new and effective approach that can classify PQDs occurring in the HPS with high recognition performance.