Model predictive yaw control using fuzzy-deduced weighting factor for large- scale wind turbines

被引：1

作者：

Zhang S. ^{[1
]}

Huang L. ^{[1
]}

Song D. ^{[2
]}

Xu K. ^{[1
]}

Yang X. ^{[1
]}

Song X. ^{[1
]}

机构：

[1] XEMC Wind power Co., Ltd, Xiangtan

[2] School of Automation, Central South University, Changsha

来源：

Energy Engineering: Journal of the Association of Energy Engineering | 2021年 / 118卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Fuzzy logic control; Weighting factor; Wind turbine; Yaw control;

D O I：

10.32604/EE.2021.014269

中图分类号：

学科分类号：

摘要：

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. © 2021, Tech Science Press. All rights reserved.

引用

页码：237 / 250

页数：13

共 25 条

[1]

Global wind report 2019, (2020)

[2]

Shafiqur R., Umar T. S., Luai M. A., Wind farm-battery energy storage assessment in grid-connected microgrids, Energy Engineering, 117, 6, pp. 294-309, (2020)

[3]

Haytham A., Ehab H. B., Performance and cost analysis of energy production from offshore wind turbines, Energy Engineering, 117, 1, pp. 41-47, (2020)

[4]

Suvash C. S., Ali M. S., Atta S., Experimental study on modal and harmonic analysis of small wind turbine blades using NACA 63-415 aerofoil cross-section, Energy Engineering, 117, 2, pp. 1-17, (2020)

[5]

Yang J., Fang L. Q., Song D. R., Su M., Yang X. B., Et al., Review of control strategy of large horizontalaxis wind turbines yaw system, Wind Energy, 233, 8, (2020)

[6]

Pedersen T. F., Gottschall J., Kristoffersen J. R., Dahlberg J. A., Yawing and performance of an offshore wind farm, Brussels, EWEA Annual Event, pp. 13-19, (2011)

[7]

Perez J. M. P., Marquez F. P. G., Tobias A., Papaelias M., Wind turbine reliability analysis, Renewable and Sustainable Energy Reviews, 23, pp. 463-472, (2013)

[8]

Fleming P. A., Scholbrock A. K., Jehu A., Davoust S., Osler E., Et al., Field-test results using a nacellemounted lidar for improving wind turbine power capture by reducing yaw misalignment, Journal of Physics:Conference Series, 524, 1, (2014)

[9]

Ouyang T., Kusiak A., He Y., Predictive model of yaw error in a wind turbine, Energy, 123, pp. 119-130, (2017)

[10]

Schlipf D., Grau P., Raach S., Duraiski R., Trierweiler J., Et al., Comparison of linear and nonlinear model predictive control of wind turbines using LIDAR, 2014 American Control Conference, pp. 3742-3747, (2014)

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