Utilizing Adversarial Training for Robust Voltage Control: An Adaptive Deep Reinforcement Learning Method

TL;DR

This paper introduces an adversarial training framework using deep reinforcement learning to improve the robustness of voltage control in distribution networks with high DER penetration against strategic cyber attacks.

Contribution

It develops a novel adversarial DRL-based voltage control method that adapts to white-box attacks, enhancing system robustness and operational stability.

Findings

Maintains voltage stability under attack scenarios

Enhances robustness against strategic cyber attacks

Demonstrates effectiveness through simulations on DER-rich networks

Abstract

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.