Experimental investigation on load mitigation of a 5 MW wind turbine in digital fluid power pitch system test rig based on predictive load mitigation controller

ABSTRACT

The pitch control system plays a vital role in generating rated power and mitigates mechanical loads during strong wind gusts. Generally, the hydraulic pitch system deployed in a large-scale wind turbine uses a proportional/servo valve to adjust the pitch angle. However, these valves suffer from low operating efficiency which leads to poor pitching performance. Thus, a high-speed digital hydraulic valve-based Digital Fluid Power Pitch System (DFPPS) test rig was proposed in this paper. Moreover, the conventional controller was not able to produce adequate pitching performance during high wind flux. Therefore, a machine learning-based pitch controller was implemented in the developed test rig. The developed DFPPS test rig comprises simulation loop (Wind Generator System (WGS) model, pitch gear model, Predictive Load Mitigation Controller (PLMC), and pitch load model), and DFPPS-hardware interfaced through an I/O interface. Here, PLMC was developed to improve the load mitigation performance without compromising the generator performance at high wind speeds. The proposed PLMC is a cascade arrangement of Recurrent Elman Neural Network (RENN) tuned Fractional-Order Proportional Integral Derivative (FOPID). Further, for collecting the optimal datasets to train the RENNs in PLMC, a Customized Particle Swarm Optimization (CPSO) algorithm was utilized. Two experiments are performed considering the developed WGS model and benchmark Fatigue Aero-elastic Structure Turbulence (FAST) simulator deployed in the DFPPS test rig. The experimental results of PLMC (experiment) were compared to simulation results PLMC (simulation). Further, the experimental output of the PLMC was compared to established controllers in the literature (Radial Basis Function tuned Fractional-Order Proportional Integral Derivative (RBF-FOPID) and baseline Proportional Integral (PI)). In the experiment using FAST, relative to RBF-FOPID, the PLMC (experiment) decreased the Standard Deviation (STD) of blade and tower moment by 81.5% and 81.4%, respectively. Consequently, compared to baseline PI, the PLMC (experiment) reduced the STD of blade and tower moment by 85.6%, and 84.4%, respectively. The proposed controller effectively mitigated the load and also the generator performance was close to the rated value than its counterparts.

KEYWORDS:

• Customized particle swarm optimization
• digital fluid power pitch system
• load mitigation
• predictive load mitigation controller
• recurrent Elman neural network

Acknowledgments

The authors would like to thank the management of the Vellore Institute of Technology-Vellore campus for providing the necessary facilities to conduct the experiments.

Disclosure statement

No potential conflict of interest was reported by the author(s).