Real-Time Performance Analyses and Optimal Gain-Scheduling Control of Offshore Wind Turbine Under Ice Creep Loads

Ziqiu Song,Yi Cheng,Yang Hu,Fanghui Tan,Jizhen Liu

doi:10.1109/access.2019.2959648

Abstract

At high latitudes, offshore wind turbines often face unfavorable loads by severe ice-induced vibrations during winter season, which may endanger facilities available on the platforms and degrade operating performance of wind turbine. Hence, it is vital to analyze the influences caused by ice loads. In this paper, based on a real-time simulation model, quantifiable analyses of performance losses due to ice creep loads are firstly studied deeply. Under ice creep loads, reliable pitch control is necessary to ensure safety and high-level power-tracking capability of modern offshore wind turbine. However, uncertain influences of ice creep loads are coupled with wind turbine operation and make it a challenge for wind turbine pitch control using traditional Proportional-Integral (PI) controller from the view of industry. As a result, improved pitch control using optimal gain-scheduling strategy is proposed to alleviate impacts of ice loads where the support vector regression algorithm is adopted to represent the strong nonlinear relationship among PI parameters under different operation conditions. For each operation point, PI parameters are optimally tuned by the particle swarm optimization algorithm. Finally, the presented nonlinear optimal gain-scheduling PI (OGS-PI) controller is applied on regulating generation power and reducing tower top displacement caused by ice creep loads based on software of Fatigue, Aerodynamics, Structures, and Turbulence, a high-fidelity wind turbine simulator. Simulation results show that unfavorable influence of ice creep loads to wind turbine operation can be significantly alleviated by the OGS-PI controller, which performs much better than the traditional PI controller.

Highlights

The 21st century witnesses a new era of rapid development of renewable energy technology
Particle Swarm Optimization (PSO) algorithm is applied for multi-objective optimization considering power tracking and mechanical loads, which can give the wind turbine (WT) better performance and relieve the mechanical loads caused by ice creep
The results are shown in Appendix B, which can be found that WT with ice loads has different optimization results compared with clean scenario and the minimum improvement is more than 10%

Summary

Introduction

The 21st century witnesses a new era of rapid development of renewable energy technology. It is estimated that more than 20% of the world’s electricity demands will be met by wind energy by 2050 [1], [2]. As offshore wind power technology has made great progress, all countries regard offshore wind power as an important development direction of renewable energy [3]–[6]. Chinese government proposed an ambitious plan that the capacity of offshore wind power reaches 30GW by 2020 [9]. The technologies of Chinese offshore wind power are in their infancy, which lacks accurately modeling, exhaustive control systems and multi-scenario optimization.

Objectives

Methods

Findings

Conclusion