Work

Stochastic model predictive control of a 10-MW semi-submersible floating offshore wind turbine: A comparative study with gain-scheduled PI, LQR and MPC controllers

Year: 2025

Type: article

Abstract: This paper proposes a stochastic model predictive control (SMPC) strategy for the collective blade pitch control of the LIFES50+ OO-Star Wind Floater Semi 10 MW turbine. The control objective is to regulate rotor speed and reduce platform motion under stochastic offshore conditions. The performance of the SMPC controller is evaluated under stochastic wind and wave conditions across below-rated and above-rated mean wind speeds and compared to that of a gain-scheduled proportional-integral (PI) controller (baseline), a linear-quadratic regulator (LQR), and a deterministic model predictive controller (MPC). At a mean wind speed of 12 m/s, the SMPC controller outperforms the other controllers by reducing the occurrence of rotor overspeed by more than 10 %, with negligible loss or a slight gain in the mean electrical generator power, along with decreased power fluctuations, reduced platform pitch motion, and lower fatigue loads. This comparative study highlights the potential of SMPC to improve control of floating offshore wind turbines and motivates further research into hierarchical and probabilistic control frameworks.

Source: Ocean Engineering

DOI: 10.1016/j.oceaneng.2025.122431

Authors: Chien Lun Hou, Wei Yu, Mao Hsiung Chiang, Po Wen Cheng

Institutions: National Taiwan University, University of Stuttgart

Language: English

Topic: Wind Turbine Control Systems

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,150