Cyber Security Issues in High Renewable Penetration Grid from a Voltage Stability Perspective

Author

K. Poli Naidu, S. Sushma, P. Karthik

Abstract

 The increasing penetration of solar photovoltaic (PV), wind generation, and other inverter-based resources is changing the operating characteristics of modern power grids. At the same time, monitoring, protection, voltage-control, and energy-management functions are becoming increasingly dependent on digital communication and control infrastructure. This integration creates a cyber-physical environment in which a cyberattack can propagate into the physical power system and potentially influence its stability [7], [10]. False data injection attacks (FDIAs) are particularly important because manipulated measurements can influence state estimation and control decisions while remaining difficult to detect using conventional residual-based methods [1], [4]. Other threats, including denial-of-service, replay, man-in-the-middle, and malicious command attacks, can also interfere with measurements, communication, or control functions [8], [10]. This paper reviews cybersecurity issues in high-renewable-penetration grids from a voltage-stability perspective. Existing statistical, machine-learning, deep-learning, graph-based, and localization-oriented detection methods are examined [3], [5], [7], [9]. The review emphasizes the increasing need for topology-aware and stability-oriented detection methods, while also highlighting several limitations, including reliance on benchmark systems, simplified cyber-network models, limited validation using real-world data, challenges posed by adaptive attacks, and the impact of converter-driven stability issues [10].Finally, a conceptual framework integrating cyberattack detection, localization, voltage-stability assessment, and risk-aware mitigation is proposed.

Keywords

Cybersecurity, false data injection attack, voltage stability, renewable energy, inverter-based resources, smart grid, cyber-physical power system, machine learning, graph-based detection.

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References

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