Torque Curve Gain Dynamic Optimization for Maximum Power Point Tracking of Wind Turbines
Zhou Lianjun1, Li Qun2, Yin Minghui1, Yang Jiongming3, Zou Yun1
1. School of Automation Nanjing University of Science and Technology Nanjing 210094 China; 2. Research Institute State Grid Jiangsu Electric Power Co. Ltd Nanjing 211103 China; 3. Jiangsu Goldwind Science & Technology Co. Ltd Yancheng 224100 China
Abstract：The optimal torque (OT) method is a common method to realize the maximum power point tracking (MPPT) of wind turbines. By modifying the torque curve gain, the wind energy capture efficiency of wind turbine operating in MPPT mode under turbulent conditions can be improved. However, there is a precondition that the torque curve gain should be reasonably designed comprehensively considering the influence of turbulence characteristics. The existing researches construct the optimal parameter model describing the relationship between optimal torque curve gain and turbulence characteristics offline, then, based on the model, realize the dynamic optimization of torque curve gain according to the time-varying turbulence characteristics online. Nevertheless, because there is a complex three-dimensional nonlinear function relation between the optimal torque curve gain and turbulence characteristic indexes including average wind speed, turbulence intensity and turbulence frequency, it is necessary to traverse search the corresponding optimal torque gain based on a large number of turbulent wind speed series with different characteristics, so as to obtain sufficient samples for constructing the optimal parameter model. The corresponding huge amount of dynamic simulation calculation is very time-consuming and difficult to be applied in batch engineering. It was found in this paper that the wind energy capture efficiency corresponding to the OT method ($P_{\text{favg}}^{\text{OT}}$) can reflect the comprehensive effect of multiple factors affecting the MPPT of wind turbine. And the results based on Spearman rank correlation analysis show that the correlation coefficient between $P_{\text{favg}}^{\text{OT}}$ and the optimal torque curve gain is greater than 0.9, which is a very strong correlation. By comparison, the Spearman correlation coefficient between optimal torque curve gain and three turbulence characteristics is about 0.5, indicates that the degree of correlation is only moderate. Therefore, it was proposed to use $P_{\text{favg}}^{\text{OT}}$ as a single-valued characterization index to construct a quantitative relationship with the optimal torque gain, which can reduce the dimension of the optimal parameter model based on three variables to a single variable. On this basis, an MPPT control method was proposed to optimize the torque curve gain online according to $P_{\text{favg}}^{\text{OT}}$ and the optimal parameter model. Since the actual wind turbine adopts an improved MPPT control method with dynamic adjustment of torque curve gain, this paper constructed a digital twin of wind turbine using the OT method in programmable logic controller (PLC), and made it run synchronously with the actual wind turbine to realize the acquisition of $P_{\text{favg}}^{\text{OT}}$. Simulation results based on FAST software developed by National Renewable Energy Laboratory show that, the proposed method reduces the time required to construct the optimal parameter model from 112.7 days to 2.3 days, not only retains the comprehensive consideration of turbulence characteristics so as to maintain high wind energy capture efficiency, but also significantly reduces the computing capacity demand and time cost, which is convenient for batch customization and rapid deployment. At the same time, based on the Beckhoff CX5130 PLC commonly used by the current batch wind turbine products, the engineering feasibility of synchronous operation of the wind turbine digital twin and actual wind turbine was tested and verified. It should be noted that, when the wind turbine model parameters used to construct the optimal parameter model and the wind turbine digital twin do not match the actual wind turbine parameters, the application effect of this method will be decreased. Therefore, how to calibrate the wind turbine model parameters based on operating data, and update the optimal parameter model and wind turbine digital twin in a timely manner remains to be further studied.
周连俊, 李群, 殷明慧, 杨炯明, 邹云. 面向风电机组最大功率点跟踪的转矩曲线增益动态优化[J]. 电工技术学报, 2023, 38(13): 3447-3458.
Zhou Lianjun, Li Qun, Yin Minghui, Yang Jiongming, Zou Yun. Torque Curve Gain Dynamic Optimization for Maximum Power Point Tracking of Wind Turbines. Transactions of China Electrotechnical Society, 2023, 38(13): 3447-3458.
[1] 许利通, 程明, 魏新迟, 等. 考虑损耗的无刷双馈风力发电系统功率反馈法最大功率点跟踪控制[J]. 电工技术学报, 2020, 35(3): 472-480. Xu Litong, Cheng Ming, Wei Xinchi, et al.Power signal feedback control of maximum power point tracking control for brushless doubly-fed wind power generation system considering loss[J]. Transactions of China Electrotechnical Society, 2020, 35(3): 472-480. [2] Mousa H H H. State of the art perturb and observe MPPT algorithms based wind energy conversion systems: a technology review[J]. International Journal of Electrical Power & Energy Systems, 2021, 126: 106598. [3] 陈载宇, 殷明慧, 蔡晨晓, 等. 一种实现风力机MPPT控制的加速最优转矩法[J]. 自动化学报, 2015, 41(12): 2047-2057. Chen Zaiyu, Yin Minghui, Cai Chenxiao, et al.An accelerated optimal torque control of wind turbines for maximum power point tracking[J]. Acta Automatica Sinica, 2015, 41(12): 2047-2057. [4] 耿华, 杨耕, 周伟松. 考虑风机动态的最大风能捕获策略[J]. 电力自动化设备, 2009, 29(10): 107-111. Geng Hua, Yang Geng, Zhou Weisong.MPPT strategy considering wind turbine dynamics[J]. Electric Power Automation Equipment, 2009, 29(10): 107-111. [5] 殷明慧, 蒯狄正, 李群, 等. 风机最大功率点跟踪的失效现象[J]. 中国电机工程学报, 2011, 31(18): 40-47. Yin Minghui, Kuai Dizheng, Li Qun, et al.A phenomenon of maximum power point tracking invalidity of wind turbines[J]. Proceedings of the CSEE, 2011, 31(18): 40-47. [6] Tang Chun, 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. [7] Johnson K, Fingersh L J, Balas M, et al.Methods for increasing region 2 power capture on a variable speed HAWT[C]//42nd AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, USA, 2004: 350. [8] 殷明慧, 张小莲, 叶星, 等. 一种基于收缩跟踪区间的改进最大功率点跟踪控制[J]. 中国电机工程学报, 2012, 32(27): 24-31, 178. Yin Minghui, Zhang Xiaolian, Ye Xing, et al.Improved MPPT control based on the reduction of tracking range[J]. Proceedings of the CSEE, 2012, 32(27): 24-31, 178. [9] 张小莲, 殷明慧, 周连俊, 等. 风电机组最大功率点跟踪控制的影响因素分析[J]. 电力系统自动化, 2013, 37(22): 15-21. Zhang Xiaolian, Yin Minghui, Zhou Lianjun, et al.Analysis on factors affecting performance of MPPT control[J]. Automation of Electric Power Systems, 2013, 37(22): 15-21. [10] 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 Lianjun, Yin Minghui, Chen Zaiyu, 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. [11] Johnson K E.Adaptive torque control of variable speed wind turbines[R]. Office of Scientific and Technical Information (OSTI), 2004. [12] 周连俊, 殷明慧, 杨炯明, 等. 考虑变化湍流风速条件的风电机组改进自适应转矩控制[J]. 电力系统自动化, 2021, 45(1): 184-191. Zhou Lianjun, Yin Minghui, Yang Jiongming, et al.Improved adaptive torque control considering varying turbulence conditions for wind turbines[J]. Automation of Electric Power Systems, 2021, 45(1): 184-191. [13] Zhang Xiaolian, Huang Can, Hao Sipeng, et al.An improved adaptive-torque-gain MPPT control for direct-driven PMSG wind turbines considering wind farm turbulences[J]. Energies, 2016, 9(11): 977. [14] Zhang Xiaolian.An improved maximum power point tracking method based on decreasing torque gain for large scale wind turbines at low wind sites[J]. Electric Power Systems Research, 2019, 176: 105942. [15] Yin Minghui, Li Weijie, Chung C Y, et al.Optimal torque control based on effective tracking range for maximum power point tracking of wind turbines under varying wind conditions[J]. IET Renewable Power Generation, 2017, 11(4): 501-510. [16] 殷明慧, 张小莲, 邹云, 等. 跟踪区间优化的风力机最大功率点跟踪控制[J]. 电网技术, 2014, 38(8): 2180-2185. Yin Minghui, Zhang Xiaolian, Zou Yun, et al.Improved MPPT control of wind turbines based on optimization of tracking range[J]. Power System Technology, 2014, 38(8): 2180-2185. [17] Zhou Lianjun, Zhang Zhengyang, Yin Minghui, et al.Indirect effects of turbulence frequency on maximum power point tracking of wind turbine[C]//10th International Conference on Advances in Power System Control, Operation & Management (APSCOM 2015), Hong Kong, China, 2015: 1-6. [18] 程细玉, 程璟. 数理统计[M]. 厦门: 厦门大学出版社, 2016. [19] 徐佳宁, 倪裕隆, 朱春波. 基于改进支持向量回归的锂电池剩余寿命预测[J]. 电工技术学报, 2021, 36(17): 3693-3704. Xu Jianing, Ni Yulong, Zhu Chunbo.Remaining useful life prediction for lithium-ion batteries based on improved support vector regression[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3693-3704. [20] 叶林, 马明顺, 靳晶新, 等. 考虑风电-光伏功率相关性的因子分析-极限学习机聚合方法[J]. 电力系统自动化, 2021, 45(23): 31-40. Ye Lin, Ma Mingshun, Jin Jingxin, et al.Factor analysis-extreme learning machine aggregation method considering correlation of wind power and photovoltaic power[J]. Automation of Electric Power Systems, 2021, 45(23): 31-40. [21] 何绍民, 杨欢, 王海兵, 等. 电动汽车功率控制单元软件数字化设计研究综述及展望[J]. 电工技术学报, 2021, 36(24): 5101-5114. He Shaomin, Yang Huan, Wang Haibing, et al.Review and prospect of software digital design for electric vehicle power control unit[J]. Transactions of China Electrotechnical Society, 2021, 36(24): 5101-5114. [22] Branlard E, Jonkman J, Dana S, et al.A digital twin based on OpenFAST linearizations for real-time load and fatigue estimation of land-based turbines[J]. Journal of Physics: Conference Series, 2020, 1618(2): 022030. [23] Jonkman J M, Buhl Jr M L. FAST user’s guide[R]. Golden, Colorado, USA: National Renewable Energy Laboratory, 2005. [24] 陈载宇, 沈春, 殷明慧, 等. 面向AGC的变速变桨风电机组有功功率控制策略[J]. 电力工程技术, 2017, 36(1): 9-14. Chen Zaiyu, Shen Chun, Yin Minghui, et al.Review of active power control strategy for variable-speed variable-pitch wind turbine participating in AGC[J]. Electric Power Engineering Technology, 2017, 36(1): 9-14. [25] Darrow P J. Wind Turbine Control Design to Reduce Capital Costs: 7 January2009 - 31 August 2009[R]. Office of Scientific and Technical Information (OSTI), 2010. [26] Chen Zaiyu, Yin Minghui, Zou Yun, et al.Maximum wind energy extraction for variable speed wind turbines with slow dynamic behavior[J]. IEEE Transactions on Power Systems, 2017, 32(4): 3321-3322. [27] Huang Can, Li Fangxing, Jin Zhiqiang.Maximum power point tracking strategy for large-scale wind generation systems considering wind turbine dynamics[J]. IEEE Transactions on Industrial Electronics, 2015, 62(4): 2530-2539. [28] 姚琦, 胡阳, 柳玉, 等. 考虑载荷抑制的风电场分布式自动发电控制[J]. 电工技术学报, 2022, 37(3): 697-706. Yao Qi, Hu Yang, Liu Yu, et al.Distributed automatic generation control of wind farm considering load suppression[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 697-706. [29] Xi Renqiang, et al.A semi-analytical model of aerodynamic damping for horizontal axis wind turbines and its applications[J]. Ocean Engineering, 2020, 214: 107861. [30] Pucci M, Cirrincione M.Neural MPPT control of wind generators with induction machines without speed sensors[J]. IEEE Transactions on Industrial Electronics, 2011, 58(1): 37-47. [31] Hong C M, Chen C H, Tu C S.Maximum power point tracking-based control algorithm for PMSG wind generation system without mechanical sensors[J]. Energy Conversion and Management, 2013, 69: 58-67. [32] 颜湘武, 王德胜, 杨琳琳, 等. 直驱风机惯量支撑与一次调频协调控制策略[J]. 电工技术学报, 2021, 36(15): 3282-3292. Yan Xiangwu, Wang Desheng, Yang Linlin, et al.Coordinated control strategy of inertia support and primary frequency regulation of PMSG[J]. Transactions of China Electrotechnical Society, 2021, 36(15): 3282-3292. [33] Iqbal A, Singh G K.PSO based controlled six-phase grid connected induction generator for wind energy generation[J]. CES Transactions on Electrical Machines and Systems, 2021, 5(1): 41-49. [34] 耿华, 杨耕. 变速变桨距风电系统的功率水平控制[J]. 中国电机工程学报, 2008, 28(25): 130-137. Geng Hua, Yang Geng.Output power level control of variable-speed variable-pitch wind generators[J]. Proceedings of the CSEE, 2008, 28(25): 130-137. [35] 赵云龙, 翁兰溪, 黄文超, 等. 基于山脊地形台风风场的铁塔风振系数研究[J]. 电气技术, 2021, 22(3): 38-43. Zhao Yunlong, Weng Lanxi, Huang Wenchao, et al.Research on wind-vibration coefficient of iron tower based on typhoon wind field of ridge terrain[J]. Electrical Engineering, 2021, 22(3): 38-43. [36] Nichita C, Luca D, Dakyo B, et al.Large band simulation of the wind speed for real time wind turbine simulators[J]. IEEE Transactions on Energy Conversion, 2002, 17(4): 523-529. [37] CSA C61400-1 Windturbines—Part 1: Design requi-rements[S]. CSA, 2014. [38] Xie Kaigui, Jiang Zefu, Li Wenyuan.Effect of wind speed on wind turbine power converter reliability[J]. IEEE Transactions on Energy Conversion, 2012, 27(1): 96-104.
2023, Vol. 38 
