MAXIMUM POWER POINT TRACKING CONTROL FOR WIND TURBINE CONSIDERING DYNAMIC PROCESS

Wang Xiaohu, Dong Jian, Liu Weichao, Zhang Linzhong, Han Xinyue

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 96-103.

PDF(2695 KB)
Welcome to visit Acta Energiae Solaris Sinica, Today is
PDF(2695 KB)
Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 96-103. DOI: 10.19912/j.0254-0096.tynxb.2024-0618

MAXIMUM POWER POINT TRACKING CONTROL FOR WIND TURBINE CONSIDERING DYNAMIC PROCESS

  • Wang Xiaohu, Dong Jian, Liu Weichao, Zhang Linzhong, Han Xinyue
Author information +
History +

Abstract

In order to improve the wind energy capture capability during the dynamic operation of wind turbines, a compound control strategy of torque error feedforward and pitch angle follow-up is proposed, and a maximum power point tracking (MPPT) controller considering the dynamic process is designed. The unscented Kalman filter is used to estimate the aerodynamic torque of the rotor, and the Newton-Raphson iteration method is used to estimate the wind speed and tip speed ratio. The difference between the estimated aerodynamic torque and the steady-state optimal torque is used as the feedforward signal to control the electromagnetic torque of the generator, and the gain scheduling of the feedforward parameter generator speed is performed.The estimated value of the tip speed ratio is used to adjust the pitch angle, so that the pitch angle reaches the optimal value under the current tip speed ratio state, and the wind energy capture capability under the non-optimal tip speed ratio state is improved. The simulation results show that the control strategy can effectively improve the dynamic response of wind turbine MPPT and the amount of wind energy capture capability.

Key words

wind turbine / controller / pitch angle / maximum power point tracking / inertial response time / torque error feedforward

Cite this article

Download Citations
Wang Xiaohu, Dong Jian, Liu Weichao, Zhang Linzhong, Han Xinyue. MAXIMUM POWER POINT TRACKING CONTROL FOR WIND TURBINE CONSIDERING DYNAMIC PROCESS[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 96-103 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0618

References

[1] HUANG C, LI F, JIN Z Q.Maximum power point tracking strategy for large-scale wind generation systems considering wind turbine dynamics[J]. IEEE transactions on sustainable energy, 2015, 6(1): 2530-2539
[2] MORREN J, PIERIK J, DE HAAN S W H. Inertial response of variable speed wind turbines[J]. Electric power systems research, 2006, 76(11): 980-987.
[3] TANG C, SOONG W L, FREERE P, et al.Dynamic wind turbine output power reduction under varying wind speed conditions due to inertia[J]. Wind energy, 2013, 16(4): 561-573.
[4] 陈家伟, 陈杰, 龚春英. 变速风力发电机组恒带宽最大功率跟踪控制策略[J]. 中国电机工程学报, 2012, 32(27): 32-38, 179.
CHEN J W, CHEN J, GONG C Y.A constant-bandwidth MPPT strategy for variable-speed WECS[J]. Proceedings of the CSEE, 2012, 32(27): 32-38, 179.
[5] 周连俊, 殷明慧, 陈载宇, 等. 考虑湍流频率因素的风力机最大功率点跟踪控制[J]. 中国电机工程学报, 2016, 36(9): 2381-2388.
ZHOU L J, YIN M H, CHEN Z Y, et al.Maximum power point tracking control of wind turbines with consideration of turbulence frequency[J]. Proceedings of the CSEE, 2016, 36(9): 2381-2388.
[6] 茅靖峰, 吴博文, 吴爱华, 等. 风力发电系统最大功率跟踪自适应鲁棒控制[J]. 电力系统保护与控制, 2018, 46(22): 80-86.
MAO J F, WU B W, WU A H, et al.Adaptive robust MPPT control for wind power generation system[J]. Power system protection and control, 2018, 46(22): 80-86.
[7] KIM K H, VAN T L, LEE D C, et al.Maximum output power tracking control in variable-speed wind turbine systems considering rotor inertial power[J]. IEEE transactions on industrial electronics, 2013, 60(8): 3207-3217.
[8] 赵骞, 邵一川, 姚兴佳, 等. 风电机组基于最优跟踪路径的改进型MPPT控制[J]. 中国电机工程学报, 2020, 40(1): 282-289, 394.
ZHAO Q, SHAO Y C, YAO X J, et al.Improved MPPT control of wind turbine based on optimal tracking path[J]. Proceedings of the CSEE, 2020, 40(1): 282-289, 394.
[9] 巩伟峥, 许凌, 姚寅. 计及风速分布与机组惯量转化不确定性的风电场可用惯量估计[J]. 上海交通大学学报, 2021, 55(S2): 51-59.
GONG W Z, XU L, YAO Y.Estimation of wind farm available inertia considering uncertainty of wind speed distribution and unit inertia transformation[J]. Journal of Shanghai Jiao Tong University, 2021, 55(S2): 51-59.
[10] 张家安, 王军燕, 刘辉, 等. 基于分量相依性的风速随机性建模方法[J]. 太阳能学报, 2024, 45(2): 109-115.
ZHANG J A, WANG J Y, LIU H, et al.Modeling method of wind speed randomness based on component dependence[J]. Acta energiae solaris sinica, 2024, 45(2): 109-115.
[11] 李大中, 李颖宇. 基于深度学习与误差修正的超短期风电功率预测[J]. 太阳能学报, 2021, 42(12): 200-205.
LI D Z, LI Y Y.Ultra-short term wind power prediction based on deep learning and error correction[J]. Acta energiae solaris sinica, 2021, 42(12): 200-205.
[12] ABDULLAH M A, YATIM A H M, TAN C W, et al. A review of maximum power point tracking algorithms for wind energy systems[J]. Renewable and sustainable energy reviews, 2012, 16(5): 3220-3227.
[13] YENDURI K, SENSARMA P.Maximum power point tracking of variable speed wind turbines with flexible shaft[J]. IEEE transactions on sustainable energy, 2016, 7(3): 956-965.
PDF(2695 KB)

Accesses

Citation

Detail

Sections
Recommended

/