Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

Abstract

Nowadays, due to the increase in the demand for electrical energy and the development of technology, the electrical devices have a more complex structure. This situation has increased the importance of concept of the power quality in the electrical power system. This paper presents a deep learning-based system to recognize the power quality disturbances (PQDs) in the solar photovoltaic (SPV) plant integrated with power system networks. The PQDs are analyzed using continuous wavelet transform (CWT) and image files are obtained from scalograms of CWT. Then, these image files are used to recognize PQDs with the help of a hybrid deep learning approach based on convolutional neural network (CNN), neighbor component analysis (NCA), and support vector machine (SVM). In this hybrid deep learning approach, the image files are given as input to AlexNet and GoogLeNet. The NCA is applied to the features obtained from the last dropout layer of each architecture. The distinctive features obtained from the NCA process are classified using the SVM algorithm. In order to evaluate the proposed approach, PQD data are obtained from a modified IEEE 13-bus test system including the SPV system. Several analyses and comparisons are carried out to verify the success of the proposed approach. It has been found that the proposed hybrid deep learning approach has the ability to accurately recognize the PQDs even if the SPV plant integrated power system has a negative effect on power quality.