Mar 30, 2023 · This paper investigates the data-driven destabilizing attack and robust defense strategy based on adversarial deep reinforcement learning for inverter-based ...

This paper investigates the data- driven destabilizing attack and robust defense strategy based on adversarial deep reinforcement learning for inverter-based mi ...

p>The droop controllers of inverter-based resources (IBRs) can be adjustable by grid operators to facilitate regulation services.

Save and organize your research references with the Papers cloud library. Access your library anytime, anywhere with the Papers web, desktop, or mobile apps ...

The droop controllers of inverter-based resources (IBRs) can be adjustable by grid operators to facilitate regulation services. Considering the increasing ...

Destabilizing Attack and Robust Defense for Inverter-Based Microgrids by Adversarial Deep Reinforcement Learning. IEEE Trans. Smart Grid 14(6): 4839-4850 ...

This paper investigates the data-driven destabilizing attack and robust defense strategy based on adversarial deep reinforcement ...

May 31, 2024 · This paper addresses challenges in the field of adversarial attacks against learning-based models in the context of smart grids.

This paper proposes a deep reinforcement learning (DRL)-based Volt- VAR co-optimization technique for reducing voltage fluctuations as well as power loss under ...

This paper studies load frequency control (LFC) for single-area power systems under cyber-attacks based on robust DRL with state-space ...