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AI-Driven Secure Load Frequency Control of Renewable Energy and Electric Vehicle Integrated Power Systems

Jul 2026 · 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT) · pp. 412-418 · 0 citations · 16 references

Abstract

The increasing integration of renewable energy sources (RES) and electric vehicles (EVs) has transformed modern power systems into complex cyber-physical networks. While these advancements enhance sustainability and flexibility, they introduce vulnerabilities to cyber-attacks such as false data injection and denial-of-service attacks. Load Frequency Control (LFC), a critical mechanism for maintaining system stability, is particularly susceptible due to its reliance on communication networks. This paper proposes an AI-based framework for real-time cyber-attack detection and mitigation in LFC systems. The proposed approach integrates a deep learning-based anomaly detection model with an adaptive control strategy to ensure resilient frequency regulation. Simulation results demonstrate improved detection accuracy (96.8%) and reduced frequency deviation compared to conventional methods. The framework effectively balances robustness and computational efficiency, making it suitable for next-generation smart grids.

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