NSGA-II Optimization Framework for Multi-Criteria Individual Pitch Control Design in Floating Offshore Wind Turbines
Lei Ren(1), Matilde Santos(2), Xin Cai(1) and J. Enrique Sierra-Garcia(3) 1. College of Mechanics and Engineering Science, Hohai University Nanjing, China. 2 Institute of Knowledge Technology, Complutense University of Madrid, Madrid, Spain. 3 Systems Engineering and Automation, University of Burgos, Burgos, Spain. 2026-06-27

Abstract

The effective implementation of Individual PitchControl (IPC) is crucial for mitigating structural loads and platform motions in floating offshore wind turbines (FOWTs), yet the tuning of its controller parameters presents a complex multi-objective challenge. This study proposes a systematic optimization framework to address this issue. A multi-objective optimization problem is formulated with the goal of simultaneously minimizing blade root fatigue loads, power fluctuations, and pitch actuator duty. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to identify the Pareto-optimal set of proportional-integral (PI) gains for the IPC system. Time-domain simulations of a reference FOWT model demonstrate that all optimized control strategies significantly outperform conventional collective pitch control, achieving a reduction of up to 27.6% in blade root load fluctuations and improved platform stability. Among them, the balanced strategy derived from the Pareto front offers the most favourable overall trade-off, providing substantial load reduction with reasonable actuator activity. The results validate the proposed framework as an effective tool for the design of high-performance IPC systems for FOWTs.

Key words: Floating Offshore Wind Turbines, Individual Pitch Control, Multi-Objective Optimization, NSGA-II, Pareto front, Load Reduction

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