Islanding power quality detection using lighting search optimization with deep learning model on distributed generation systems

Abstract

Recently, the significant penetration of distributed generations (DGs) in distribution networks has made new challenging issues in network protection. A major challenge exists in the distribution networks in the identification of islanding conditions, where a portion of the distribution network with DGs is accidentally detached from the central grid due to protection relay and circuit breaker operation. Since it results in damaging the DGs and their equipment, effective islanding condition detection model becomes essential. Besides, non-detected islanding conditions can lead to voltage and frequency deviances from the normal ranges, inappropriate operation of protection, and personnel hazards. Therefore, the recent developments in deep learning (DL) can be applied to perform island power quality detection process. In this view, this article introduces an Islanding Power Quality Detection using Lighting Search Optimization with Deep Learning (IPQD-LSODL) model. The proposed IPQD-LSODL model mainly aims to find the events in islanding power quality (IPQ) and non-islanding power quality (NIPQ). The proposed model initially applies down-sampling empirical mode decomposition (DEMD) approach which effectively filters out the basic signal from the polluted ones. In addition, deep belief network (DBN) model is used to classify the events into IPQ and NIPQ. Moreover, the hyperparameters of the DBN method were optimally chosen by the use of LSO algorithm with an intention of accomplishing maximum classification accuracy. The performance validation of the IPQD-LSODL model is carried out and validated through comparison study with existing models. The IPQD-LSODL method achieved an accuracy of 99.91 percent in the class 5. The results implied the promising performances of the IPQD-LSODL method over recent approaches.