Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control
For wind energy operators, this work provides a method to improve safety and robustness of plant-level control against measurement errors and cyberattacks.
This paper addresses the problem of sensor errors and cyberattacks in wind turbine fleet control by training a plant controller with an adversarial agent. The Arms Race adversarial training approach reduced worst-case power loss from 39% to a 7.9% power gain relative to baseline.
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.