Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems
Qi Li, Jinan Zhang, Junbo Zhao, Jin Ye, Wen‐Zhan Song, Fangyu Li
Abstract
Development of a cyber security strategy for the active distribution systems is challenging due to the inclusion of distributed renewable energy generations. This paper proposes an adaptive hierarchical cyber attack detection and localization framework for distributed active distribution systems via analyzing electrical waveforms. Cyber attack detection is based on a sequential deep learning model, via which even minor cyber attacks can be identified. The two-stage cyber attack localization algorithm first estimates the cyber attack sub-region, and then localize the specified cyber attack within the estimated sub-region. We propose a modified spectral clustering-based network partitioning method for the hierarchical cyber attack ‘coarse’ localization. Next, to further narrow down the cyber attack location, a normalized impact score based on waveform statistical metrics is proposed to obtain a ‘fine’ cyber attack location by characterizing different waveform properties. Finally, compared with classical and state-of-art methods, a comprehensive quantitative evaluation with two case studies shows promising estimation results of the proposed framework.