Edge Resilient Control for Feeder Automation System Under Hybrid Attacks: A Hybrid Physical Model-Driven and Data-Driven Approach

The extensive application of the distributed feeder automation system (DFAs) is a prominent feature of the power distribution system (PDS). To enhance the resilience of DFAs to cyberattacks and hybrid attacks (contains both physical and cyberattacks), this paper proposes an edge resilient control approach (ERCA) for DFAs, which integrates attack detection and mitigation. Two key modules are developed: 1) Edge Cyberattack Detection and Correction Module (ECDCM), achieving quick cyberattack detection and correction based on machine learning and physical constraints; 2) Consensus-based Fault Section Localization Module (CFSLM), which incorporates causality function related to agent overcurrent states and fault location to correctly localize the fault section in hybrid attack scenarios. In addition, the effective boundaries of ERCA are theoretically deduced in this paper, which proves that ERCA can retain resilience to cyberattacks and hybrid attacks even if undetected cyberattacks exist due to the failures of machine learning-based detection submodules. Finally, the proposals effectiveness and performance are tested and verified using the IEEE 123-bus system and the real two-feeder test platform with DFAs. By adopting ERCA, outages and load losses caused by cyber attacks are 100% avoided, and the fault caused by physical attack is correctly isolated with more than 97.6% reliability in 100,000 times-level Monte Carlo Numerical simulations of hybrid attacks.