Multi-objective Bayesian optimisation over sparse subspaces for model predictive control of wind farms
Year: 2025
Type: article
Abstract: As model mismatch is inevitable, fine-tuning advanced control strategies like model predictive control (MPC) using closed-loop performance data is critical for increased closed-loop control of wind farms (WFs). However, challenges such as conflicting WF control objectives, limited closed-loop data due to expensive experiments, and the high-dimensional design spaces of these MPC formulations make tuning non-trivial. Inspired by the notion of performance-oriented learning, we propose a multi-objective (MO) Bayesian optimisation (BO) framework over sparse subspaces to address these challenges systematically for increased closed-loop MPC performance. To show the efficacy of the BO approach, a simulation case study with a 3x3 WF is investigated where the control objective is to provide secondary frequency regulation while minimising dynamic loading for an MPC with 28 design parameters to auto-tune. Simulation results show that the proposed framework achieves a good balance between the two conflicting WF control objectives, where dynamic loading is reduced by 51.59% compared to a nominal MPC whose performance is not tuned using closed-loop data while still achieving similar tracking performance. The proposed method is general and can be applied regardless of a closed-loop control goal, WF specifications (complexity, topology, location, size), or controller formulation for multi-objective constrained control of WFs.
Source: Renewable Energy
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,460
Award: G1403394375
OpenAlex ID: W4409200117