Wind farm control (WFC) plays a crucial role in mitigating the wake effect, the negative aerodynamic interactions among wind turbines. Recent advances in data-driven control and artificial intelligence offer new opportunities to design more intelligent WFC systems, motivating the need for a tool that supports interactive design and validation in simulation. To address this, we present ffconnect, a general, open-source Python-based interface for FAST.Farm, a mid-fidelity wind farm simulator. Compared to prior work, ffconnect introduces a restructured Application Programming Interface (API) with enriched state access and supports integrating FAST.Farm with modern scientific computing and machine learning ecosystems by building entirely on Python. In experiments, ffconnect shows negligible runtime overhead compared to the original FAST.Farm across a range of simulation lengths and farm sizes, and demonstrates its effectiveness through a yaw-tracking case study. Finally, we provide the source code of ffconnect to keep it accessible for general users.
The rapid expansion of offshore wind energy is central to the European Union's climate-neutrality targets, with High Voltage Direct Current-connected offshore wind power plants (HVDC-OWPPs) becoming increasingly important for integrating gigawatt-scale renewable generation over l…
Motion planning algorithms compute control sequences that drive autonomous robots to goal regions while avoiding unsafe states. Existing methods, from sampling-based planning to deep reinforcement learning, typically provide task-completion guarantees only with respect to a nomin…
This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Awa…
This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and…
This paper introduces a novel multivariate Transformer \emph{StateFormer} that forecasts degradation dynamics of large-scale battery systems. The model learns across time scales, from short-term thermal fluctuations to long-term aging trajectories, enabling accurate prediction of…
We introduce trajectory-regularized stochastic optimal control (TRSOC), which augments standard stochastic optimal control (SOC) with a Kullback--Leibler (KL) divergence between controlled and reference trajectory distributions. Using Girsanov's theorem, the trajectory KL reduces…