Model Predictive Yaw Control Using Fuzzy-Deduced Weighting Factor for Large- Scale Wind Turbines
Open Access
- 1 January 2021
- journal article
- Published by Computers, Materials and Continua (Tech Science Press) in Energy Engineering
- Vol. 118 (2), 237-250
- https://doi.org/10.32604/ee.2021.014269
Abstract
Yaw control system plays an important role in helping large-scale horizontal wind turbines capture the wind energy. To track the stochastic and fast-changing wind direction, the nacelle is rotated by the yaw control system. Therein, a difficulty consists in the variation speed of the wind direction much faster than the rotation speed of the nacelle. To deal with this difficulty, model predictive control has been recently proposed in the literature, in which the previewed wind direction is employed into the predictive model, and the estimated captured energy and yaw actuator usage are two contradictive objectives. Since the performance of the model predictive control strategy relies largely on the weighting factor that is designed to balance the two objectives, the weighting factor should be carefully selected. In this study, a fuzzy-deduced scheme is proposed to derive the weighting factor of the model predictive yaw control. For the proposed fuzzy-deduced strategy, the variation degree and the increment of the wind direction during the predictive horizon are used as the inputs, and the weighting factor is the output, which is dynamically adjusted. The proposed model predictive yaw control is demonstrated by some simulations using real wind data and its performance is compared with the conventional model predictive control with the fixed weighting factor. Comparison results confirm the outweighing performance of the proposed control strategy over the conventional one.Keywords
This publication has 18 references indexed in Scilit:
- Maximum Wind Energy Extraction for Variable Speed Wind Turbines With Slow Dynamic BehaviorIEEE Transactions on Power Systems, 2017
- Model predictive control with finite control set for variable-speed wind turbinesEnergy, 2017
- Predictive model of yaw error in a wind turbineEnergy, 2017
- Inertia compensation scheme for wind turbine simulator based on deviation mitigationJournal of Modern Power Systems and Clean Energy, 2016
- P300-based Brain–Computer Interface with Latency Estimation Using ABC-based Interval Type-2 Fuzzy Logic SystemInternational Journal of Fuzzy Systems, 2016
- A PSO-Based Fuzzy-Controlled Searching for the Optimal Charge Pattern of Li-Ion BatteriesIEEE Transactions on Industrial Electronics, 2014
- Field-test results using a nacelle-mounted lidar for improving wind turbine power capture by reducing yaw misalignmentJournal of Physics: Conference Series, 2014
- Wind turbine reliability analysisRenewable and Sustainable Energy Reviews, 2013
- Model predictive control of a wind turbine using short‐term wind field predictionsWind Energy, 2012
- Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain GainIFAC Proceedings Volumes, 2012