Islanding detection in microgrid using deep learning based on 1D CNN and CNN-LSTM networks

Abstract

Islanding detection is a critical task due to safety hazards and technical issues for the operation of microgrids. Deep learning (DL) has been applied for islanding detection and achieved good results due to the ability of automatic feature learning in recent years. Long short term memory (LSTM) and two dimensional (2D) convolutional neural networks (CNN) based DL techniques are implemented and demonstrated well performance on islanding detection. However, one dimensional (1D) CNN is more suitable for real-time implementations since it has relatively low complexity and cost-effective than 2D CNN. In this paper, for the first time, the 1D CNN and the combination of 1D CNN-LSTM are proposed for islanding detection to better exploit the global information of islanding data samples using the strengths of both networks. The proposed methods utilize only voltage and current harmonic measurements as input at the point of common coupling (PCC). About 4000 cases under the modified CERTS microgrid model are simulated to evaluate the performance of the proposed architectures. The simulation results and the presented analysis show that the proposed networks have achieved the maximum accuracy of 100% on the task of islanding detection; especially the proposed CNN-LSTM model outperforms the other approaches. Furthermore, the robustness of the proposed methods is demonstrated with unseen samples under low none detection zone and the expansion of microgrid topology. • This paper presents a novel 1D CNN-LSTM based deep learning method for islanding detection by using only harmonic measurements at the PCC. • The proposed method is robust to power quality disturbances and sudden changes. • The robustness of the proposed method is verified under the expansion of DGs with the six hundred unseen islanding and non-islanding samples. • The effectiveness of the proposed methods is proven under small power mistmaches, by testing with two hundred unseen events.