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Author

Athira M. Mohan

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2026

Secure Observer-Based Event-Triggered Control of Microgrid Load Frequency Control System Under DoS Attack

This study proposes a secondary remote observer-based DoS attack tolerant event-triggered control framework for an islanded microgrid load frequency control system with virtual inertia/auxiliary control subsystem while accounting for multiple practical challenges. In this work, a microgrid configuration with a remotely located secondary controller and state observer is considered, and an event-triggered communication mechanism is adopted to enhance communication efficiency in the secondary control loop. The secondary observer and controller gains adhering to a prescribed $H_{\infty}$ performance bound are derived leveraging Lyapunov-Krasovskii functional-based stability analysis and DoS attack tolerance is achieved using a modified event-triggering condition accounting DoS-induced extra output error. The stability conditions are derived by considering key system challenges, including the time-varying secondary measurement path transmission delay, event-triggering condition, remote implementation of the observer, and additional output error induced by DoS adversary. Finally, the efficacy of the proposed secondary control approach is demonstrated using Monte-Carlo simulation across different disturbance scenarios.

Athira M. Mohan, N. Meskin · 0 citations
#reinforcement learning Open access Dec 2026

A cascaded PID-reinforcement learning-based virtual inertia control in microgrid load frequency control system using electric vehicle energy storage

The islanded microgrids increasingly depend on renewable energy sources for power generation and introduce significant frequency control challenges due to the renewable sources’ intermittent nature and low system inertia. Traditional energy storage systems, though employed and effective for frequency stabilization, are often limited by high costs and power density requirements. Accounting issues of microgrid frequency performance under stochastic renewable energy source integration and limitations of conventional energy storage systems, this paper proposes a virtual inertia control strategy that leverages electric vehicle battery storage, supported by a cascade proportional integral derivative-reinforcement learning-based auxiliary controller, to enhance frequency regulation of the load frequency control system in an islanded microgrid. The proposed cascade proportional integral derivative-reinforcement learning-based virtual inertia controller is implemented and tested in a MATLAB/Simulink environment and evaluated under diverse operating conditions involving dynamic load and renewable energy source disturbances, while comparing with other control strategies. The comparative results demonstrate that the proposed virtual inertia control strategy outperforms other compared controllers in terms of frequency stability and overall dynamic response.

Athira M. Mohan, A. Khandakar, S. Muyeen · 0 citations

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