Cyber-Resilient Controllers for Smart Distribution Grid Control Layers

[EN] This paper presents a novel cyber-resilient control strategy for enhancing the operational security of future smart distribution systems (SDSs) against compromised control setpoints originating from higher-level controllers. The proposed framework addresses the structure, control architecture,...

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Detalhes bibliográficos
Autores: Chandramathi, Jishnu Sankar Vijayasekharan, Nair, Manjula G., Álvarez, Carlos|||0000-0002-8238-1606
Formato: artículo
Fecha de publicación:2025
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/226653
Acesso em linha:https://riunet.upv.es/handle/10251/226653
Access Level:acceso abierto
Palavra-chave:Cyber anomaly
Cyber resilience
Droop control
Hierarchical control
Microgrid cluster
Microgrid
Smart distribution grid
System of systems
Descrição
Resumo:[EN] This paper presents a novel cyber-resilient control strategy for enhancing the operational security of future smart distribution systems (SDSs) against compromised control setpoints originating from higher-level controllers. The proposed framework addresses the structure, control architecture, and cyber vulnerabilities of SDSs by embedding an anomaly detection and autonomous response mechanism within each control layer. An artificial neural network (ANN)-based detector is employed to identify non-implementable or malicious control commands based on local measurements and grid location data. Upon detecting a cyber anomaly, the controller avoids disconnection and enables droop-based autonomous operation, ensuring continued grid support. The proposed strategy was validated using MATLAB/Simulink R2022a under various dynamic test scenarios, demonstrating its ability to maintain system stability. Unlike prior studies that rely on offline anomaly detection, this study presents a real-time capable closed-loop control solution that detects anomalies during simulation runtime. The proposed method rejects erroneous commands arising from both cyber intrusions and human errors, thereby enhancing the cyber-resilience and reliability of SDS operations.