用对抗性传感器错误训练风场控制器，提升系统鲁棒性。
Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control
- 通过对抗训练让控制器适应恶意传感器干扰
- 最坏情况下发电损失从39%降至7.9%增益
- 适合关注风电系统安全与抗攻击的工程师
机组级控制是新兴的风能技术，通过中央控制器协同调控风机以提升风电场效率。但测量误差或黑客篡改遥测信号可能危及控制过程。本文提出一种框架，通过训练一个旨在干扰控制器的对抗代理来开发安全的植物控制器。这需要反复优化对抗者与控制器，形成类似“军备竞赛”的循环逻辑。研究对比了三种联合训练方法，发现军备竞赛策略效果最佳。初步结果表明，该方法将最坏情况下的功率损失从39%降低至7.9%的增益，显著优于基线运行策略。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。