Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control

Julian Quick; Marcus Binder Nilsen; Andreas Bechmann; Tran Nguyen Le; Pierre-Elouan Mikael Rethore

cs.LGApr 9, 2026

Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control

Authors: Julian Quick, Marcus Binder Nilsen, Andreas Bechmann, Tran Nguyen Le, Pierre-Elouan Mikael Rethore

Organizations: Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark · Department of Engineering Technology and Didactics, Technical University of Denmark, Ballerup, Denmark

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.

Figures & tables

Figure 1: Training setups: Arms Race (left), Synthetic Self Play (middle), and Self-Play (right).

Figure 2: Snapshot of the wind farm flow field at hub height. Faster speeds are shown with brighter colors. The yaw controller of the upstream wind turbine has been confounded to sense an incorrect wind direction.

Variable	Max Bias ( $\pm$ )	White Noise ( $\sigma$ )
Wind Speed (m/s)	4.0	0.5
Wind Direction ( ∘ )	10.0	2.0
Yaw Offset ( ∘ )	20.0	0
Power (MW)	0.5	0

Table 1: Noise Model Parameters. The Max Bias (

\epsilon_{max}

) applies to both the Adversarial Agent and the Procedural Noise baseline. The Gaussian standard deviation (

\sigma

) applies only to the Procedural Noise baseline.

Parameter	Value	Description
Algorithm	PPO	Proximal Policy Optimization
Learning Rate	$3\times 10^{-4}$	Step size for the optimizer
Batch Size	64	Samples per gradient update
Gamma ( $\gamma$ )	0.99	Discount factor
Steps per Iteration	250,000	Timesteps per training round

Table 2: PPO Hyperparameters used for training protagonist and adversary agents

Figure 3: Arms Race training progress evaluation matrix. For each combination of considered protagonist and adversary, a standard set of trials is used to measure the performance of the wind farm system relative to the baseline no-steering/greedy operational case. The average increase over baseline operation, and the associated standard error, are reported.

Figure 4: Summary plots comparing the different training approaches.

Figure 5: Cross-comparison results. The most formidable protagonists and adversaries identified in each training method, as well as a procedurally trained controller, are pitted against each other in five standard flow cases. Brighter colors show larger mean reward values.

Panel of Figure 6 — Figure 6: (a) Procedurally-trained protagonist agent versus Self-Play adversarial noise agent #6 (b) Arms Race protagonist agent #7 versus Self-Play adversarial agent #6. The left columns show the upstream turbine and the right columns show the downstream turbine. The true and sensed wind direction, wind speed, power, and yaw are shown in dashed red and solid black lines, respectively. The grey clouds show the maximum error allowed within the adversarial environment and the maximum bias allowed in the procedural environment.

Robust Control

Robust control designs feedback that maintains stability, safety, and performance despite disturbances, uncertainty, model errors, and changing conditions.

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Oct 27, 2026J/K move · Enter open · S save

Noise Steering: Exploiting Wake Steering and Acoustic Directivity for Noise-Constrained Wind Farm Control via Reinforcement Learning

Martín de Frutos, Baptiste Corban, Claire Bizon Monroc, Ana Bušić+3

ETSIAE-UPM - School of Aeronautics, Universidad Politécnica de Madrid, Plaza Cardenal Cisneros 3, Madrid, E-28040, Spain · Inria, 48 rue Barrault, Paris, 75013, France · DI ENS, Ecole Normale Supérieure, PSL Research University, Paris, France+3

Optimal Control RL Control

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Existing noise mitigation strategies for wind farms usually consist in reducing turbines power output, without accounting for the wake interactions between them and the noise emission directivity. This leads to over-conservative policies and unexploited wind power. Yet, turbine yaw can simultaneously affect wake interactions and the direction of noise emission, offering an additional degree of freedom for farm-level noise control. We therefore propose Noise Steering, a model-free reinforcement learning (RL) strategy that jointly controls turbine yaw angles and power setpoints to maximise farm power while satisfying a local noise constraint in a defined region. To enable training at a reduced computational cost, a low-fidelity acoustic model is coupled with the steady-state FLORIS simulator. The RL agent effectively learns how to redirect wakes and noise directivity away from the protected region while redistributing power across the farm. On a six-turbine test farm, the policy satisfies the 40 dB noise constraint throughout the full range of turbulent winds, with an average power deficit of only 2% relative to greedy unimpeded control, and up to 7.7% at high wind speeds. In comparison, the industry-standard noise-reduced operation (NRO) baseline here generates an average power loss of 17%. When deployed in the higher-fidelity FAST.Farm simulator, the policy largely retains its performance under steady and turbulent inflow, despite differences in simulation fidelity. These results demonstrate the potential of combining yaw control and power derating through RL to improve the power-noise trade-off in wind farm operation.

Abstract

Robust Control

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