Abstract

With the increasing integration of distributed energy resources (DER) into the distributed power system, the security of the power system from cyber-attacks is paramount. Cyber attacks make unintentional islanding detection challenging within the networked DER-integrated microgrid system. This study proposes a physics-informed approach for feature extraction and dimension reduction, leveraging principles from physics and domain-specific knowledge to analyze three-phase voltage and current signals. Moreover, a recurrent neural network (RNN) based gated recurrent unit (GRU), is introduced to fortify networked DER-integrated microgrids against cyber-physical threats, particularly focusing on unintentional islanding triggered by cyber-attacks at the point of common coupling (PCC). The most essential and difficult stage in an intelligent islanding detection system (IIDM) is feature extraction and selection, for which a physics-informed wavelet scattering network (WSN) and minimum redundancy maximum relevance (MRMR) algorithm are proposed. The WSN facilitates enhanced signal representation by capturing low and high-frequency information simultaneously, ensuring translation-invariant and deformationstable signal representations. The MRMR algorithm is applied for dimension reduction, ensuring that the reduced feature space retains the most informative and physically relevant features while minimizing redundancy. Finally, GRU network is proposed for islanding detection. We developed an API integrated with RT-Lab to generate a diverse dataset for islanding, faults, and nonislanding scenarios to gather PCC voltage and current signals. The proposed method is assessed under various islanding, faults, and non-islanding scenarios, also considering the non-detection zone (NDZ), a critical factor affecting islanding conditions using an OPAL-RT real-time digital simulator. The proposed method is also validated using accuracy, selectivity, and sensitivity performance indices, demonstrating the effectiveness of physicsinformed feature extraction/selection and GRU-based islanding detection (PI-IIDM), ensuring accurate islanding detection.