Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control

2026-04-09

AI Summary

This study addresses performance degradation and security risks in wind farm centralized control caused by sensor measurement errors or malicious tampering of telemetry signals. To mitigate these issues, the authors propose a security-aware control method based on adversarial reinforcement learning. The approach employs an arms-race-style adversarial training framework, wherein a primary controller and an adversarial agent engage in iterative co-optimization through repeated gameplay, effectively simulating worst-case attack or fault scenarios. This methodology substantially enhances system robustness, transforming a 39% power loss under the most adverse conditions into a 7.9% power gain—a significant improvement over baseline control strategies.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Plant-level control is an emerging wind energy technology that presents opportunities and challenges. By controlling turbines in a coordinated manner via a central controller, it is possible to achieve greater wind power plant efficiency. However, there is a risk that measurement errors will confound the process, or even that hackers will alter the telemetry signals received by the central controller. This paper presents a framework for developing a safe plant controller by training it with an adversarial agent designed to confound it. This necessitates training the adversary to confound the controller, creating a sort of circular logic or"Arms Race."This paper examines three broad training approaches for co-training the protagonist and adversary, finding that an Arms Race approach yields the best results. These initial results indicate that the Arms Race adversarial training reduced worst-case performance degradation from 39% power loss to 7.9% power gain relative to a baseline operational strategy.

Problem

• adversarial sensor errors
• wind turbine fleet control
• plant-level control
• cyber-physical security
• robust control

Innovation

• adversarial training
• wind turbine fleet control
• arms race
• robust control
• sensor error resilience