Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control

TL;DR

This paper introduces an adversarial training framework for wind turbine control systems to enhance safety and robustness against sensor errors and malicious interference, demonstrating significant performance improvements.

Contribution

It proposes an adversarial training approach, especially the Arms Race method, to improve wind farm control robustness against sensor errors and cyber threats.

Findings

Arms Race training reduces worst-case power loss from 39% to 7.9%.

Adversarial training enhances control system resilience.

Framework addresses both measurement errors and hacking threats.

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.