Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control

arXiv cs.LG / 4/13/2026

💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper proposes an adversarial-training framework for plant-level wind turbine fleet control to make centralized controllers robust to sensor measurement errors and potential telemetry tampering.
It trains an adversarial agent specifically to confound the controller, creating an “Arms Race” co-training loop to harden the control policy against worst-case disturbances.
The authors compare three co-training approaches for the protagonist controller and adversary, concluding that the Arms Race strategy performs best.
Initial results suggest the approach can dramatically reduce worst-case performance degradation, improving from a 39% power loss baseline to about a 7.9% power gain under worst-case conditions.
The work frames safe fleet control as a game between attacker-like perturbations and defensive controller learning, highlighting a practical path toward more resilient energy automation.

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.