Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution System
Year: 2025
Type: article
Abstract: The increased integration of distributed energy resources (DERs) in Active Distribution Systems (ADS)enhances grid flexibility but also introduces new cybersecurity vulnerabilities. Cyber-attacks on such systemscan disrupt grid operations, leading to energy instability or blackouts. This paper proposes an adaptivehierarchical framework for cyber-attack detection and localization in ADS. The proposed system combinesdeep learning with graph-based spectral clustering and statistical impact scoring to identify and locate cyberintrusions in near-real-time. Detection is achieved using a sequential deep neural network trained on electricalsignal anomalies, while localization is carried out in two stages—first, using spectral clustering for regionaldetection, and then using waveform-based metrics for precise node identification. The system’s performanceis validated using simulation data from IEEE 37-node feeders with injected false data and coordinated attackscenarios. Results demonstrate superior detection accuracy, localization precision, and scalability comparedto conventional intrusion detection systems. This work contributes toward building a resilient smart grid byenabling real-time situational awareness and rapid response
Author: K.Padmanaban, Malempati Ravichandra
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930820304
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