Utilizing Adversarial Training for Robust Voltage Control: An Adaptive Deep Reinforcement Learning Method
This work addresses the problem of cyber security for voltage control in modern distribution systems, offering an incremental improvement by applying adversarial training to deep reinforcement learning.
The paper tackled the vulnerability of conventional voltage control methods to strategic cyber attacks in distribution networks with high distributed energy resources by developing a robust voltage control framework using adversarial deep reinforcement learning, resulting in maintained voltage stability and operational efficiency under realistic attack scenarios.
Adversarial training is a defense method that trains machine learning models on intentionally perturbed attack inputs, so they learn to be robust against adversarial examples. This paper develops a robust voltage control framework for distribution networks with high penetration of distributed energy resources (DERs). Conventional voltage control methods are vulnerable to strategic cyber attacks, as they typically consider only random or black-box perturbations. To address this, we formulate white-box adversarial attacks using Projected Gradient Descent (PGD) and train a deep reinforcement learning (DRL) agent adversarially. The resulting policy adapts in real time to high-impact, strategically optimized perturbations. Simulations on DER-rich networks show that the approach maintains voltage stability and operational efficiency under realistic attack scenarios, highlighting the effectiveness of gradient-based adversarial DRL in enhancing robustness and adaptability in modern distribution system control.