Offshore Wind Farm Wake Deflection Control Based on Adaptive Wind Condition Prediction Error

doi:10.11930/j.issn.1004-9649.202303020

Abstract

Abstract:

Wind farm wake deflection control is an important tool to reduce the wake effect and improve the total power generation. The wind prediction is an important input to the wind farm wake deflection control, and its error has a huge impact on the actual control effect, even leading to a "decrease instead of an increase" in the overall power generation, which greatly limits the engineering application of wind farm wake deflection control technology. Therefore, this paper explores the impact of minute-level wind speed and wind direction prediction errors on the wind farm wake deflection control effect of an offshore wind farm, and proposes an offshore wind farm wake deflection control based on adaptive wind condition prediction error and a control model based on deep neural network. The results show that the total power generation of the proposed method is improved by 1.77% compared with the conventional wind farm wake deflection control method without wind prediction error adaption.

Key words: offshore wind farm, wake control, yaw angle, wind condition prediction, error adaptation, deep neural network

Jie YAN, Jialin YANG, Hangyu WANG, Jiaoyang LU, Yongqian LIU, Lei ZHANG. Offshore Wind Farm Wake Deflection Control Based on Adaptive Wind Condition Prediction Error[J]. Electric Power, 2024, 57(3): 190-196.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202303020

https://www.electricpower.com.cn/EN/Y2024/V57/I3/190

Figures/Tables 10

Table 1 Scene setting

风向	风速/(m∙s^–1)	风速预测误差/(m∙s^–1)	风向预测误差/(°)
主导/非主导	5	[–1, 1]，分辨率为0.1	—
	6
	7
	8
	9
主导/非主导	5	—	[–10, 10]，分辨率为1
	6
	7
	8
	9

Fig.1 Influence of wind speed prediction error on power increase rate (non-dominant wind direction)

Fig.2 Influence of wind speed prediction error on power increase rate (dominant wind direction)

Fig.3 Influence of wind direction prediction error on power increase rate (non-dominant wind direction)

Fig.4 Influence of wind direction prediction error on power increase rate (dominant wind direction)

Fig.5 Control strategy framework

Fig.6 Neuron node

Fig.7 DNN model structure

Fig.8 Comparison of power increase rate

Fig.9 Power increase rate of the proposed model and model 1 under the test set wind condition

References 25

1	房方, 梁栋炀, 刘亚娟, 等. 海上风电智能控制与运维关键技术[J]. 发电技术, 2022, 43 (2): 175- 185. DOI
	FANG Fang, LIANG Dongyang, LIU Yajuan, et al. Key technologies for intelligent control and operation and maintenance of offshore wind power[J]. Power Generation Technology, 2022, 43 (2): 175- 185. DOI
2	葛畅, 阎洁, 刘永前, 等. 海上风电场运行控制维护关键技术综述[J]. 中国电机工程学报, 2022, 42 (12): 4278- 4291.
	GE Chang, YAN Jie, LIU Yongqian, et al. Review of key technologies for operation control and maintenance of offshore wind farm[J]. Proceedings of the CSEE, 2022, 42 (12): 4278- 4291.
3	HUSIEN W, EL-OSTA W, DEKAM E. Effect of the wake behind wind rotor on optimum energy output of wind farms[J]. Renewable Energy, 2013, 49, 128- 132. DOI
4	SANDERSE B, PIJL S P, KOREN B. Review of computational fluid dynamics for wind turbine wake aerodynamics[J]. Wind Energy, 2011, 14 (7): 799- 819. DOI
5	POLSSTER M. Comprehensive comparison of analytical wind turbine wake models with wind tunnel measurements and wake model application on performance modelling of a downstream turbine[D]. Trondheim: Norwegian University of Science and Technology, 2017: 1–19.
6	顾波. 风电场尾流快速计算及场内优化调度研究[D]. 北京: 华北电力大学, 2016.
	GU Bo. Wake fast-calculation and power optimal dispatch for wind farms[D]. Beijing: North China Electric Power University, 2016.
7	杨志超, 高丙团, 叶飞, 等. 基于尾流效应的风电场最大出力优化控制[J]. 电力建设, 2017, 38 (4): 96- 102. DOI
	YANG Zhichao, GAO Bingtuan, YE Fei, et al. Maximum output optimal control of wind farm based on wake effect[J]. Electric Power Construction, 2017, 38 (4): 96- 102. DOI
8	邓智文, 郭苏, 许昌, 等. 海上风电场功率提升和疲劳平衡综合优化控制[J]. 太阳能学报, 2021, 42 (1): 180- 186.
	DENG Zhiwen, GUO Su, XU Chang, et al. Comprehensive optimization control of power boost and fatigue balance for offshore wind farms[J]. Acta Energiae Solaris Sinica, 2021, 42 (1): 180- 186.
9	宁旭, 曹留帅, 万德成. 基于尾流模型的风场偏航控制优化研究[J]. 海洋工程, 2020, 38 (5): 80- 90.
	NING Xu, CAO Liushuai, WAN Decheng. Study of wind farm optimization through yaw control based on analytical wake model[J]. The Ocean Engineering, 2020, 38 (5): 80- 90.
10	焦鑫, 陈为国, 张志军, 等. 基于偏航尾流模型的风场尾流主动控制[J]. 自动化技术与应用, 2020, 39 (5): 110- 114. DOI
	JIAO Xin, CHEN Weiguo, ZHANG Zhijun, et al. Active control of wind field wake based on yaw wake model[J]. Techniques of Automation and Applications, 2020, 39 (5): 110- 114. DOI
11	DOU B Z, QU T M, LEI L P, et al. Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model[J]. Energy, 2020, 209, 118415. DOI
12	SUN H Y, QIU C Y, LU L, et al. Wind turbine power modelling and optimization using artificial neural network with wind field experimental data[J]. Applied Energy, 2020, 280, 115880. DOI
13	YANG S H, DENG X W, TI Z L, et al. Cooperative yaw control of wind farm using a double-layer machine learning framework[J]. Renewable Energy, 2022, 193, 519- 537. DOI
14	王航宇. 基于风过程的风电场尾流优化控制方法研究[D]. 北京: 华北电力大学, 2022.
	Wang Hangyu. Research on the optimal control method of wind farm wake based on wind process [D]. Beijing: North China Electric Power University, 2022.
15	FLEMING P, KING J, DYKES K, et al. Initial results from a field campaign of wake steering applied at a commercial wind farm[J]. Wind Energy Science, 2019, 4 (2): 273- 285. DOI
16	刘永前, 周家慷, 阎洁, 等. 基于多任务学习的风速实时预测方法[J]. 可再生能源, 2021, 39 (4): 481- 487. DOI
	LIU Yongqian, ZHOU Jiakang, YAN Jie, et al. Wind speed real-time prediction method based on multi-task learning[J]. Renewable Energy Resources, 2021, 39 (4): 481- 487. DOI
17	张金良, 刘子毅. 基于混合模型的超短期风速区间预测[J]. 电力系统保护与控制, 2022, 50 (22): 49- 58.
	ZHANG Jinliang, LIU Ziyi. Ultra short term wind speed interval prediction based on a hybrid model[J]. Power System Protection and Control, 2022, 50 (22): 49- 58.
18	姜明洋, 徐丽, 张开军, 等. 基于季节指数调整的循环神经网络风速时间序列预测[J]. 太阳能学报, 2022, 43 (2): 444- 450.
	JIANG Mingyang, XU Li, ZHANG Kaijun, et al. Recurrent neural network prediction of wind speed time series based on seasonal exponential adjustment[J]. Acta Energiae Solaris Sinica, 2022, 43 (2): 444- 450.
19	唐振浩, 赵赓楠, 曹生现, 等. 基于SWLSTM算法的超短期风向预测[J]. 中国电机工程学报, 2019, 39 (15): 4459- 4468.
	TANG Zhenhao, ZHAO Gengnan, CAO Shengxian, et al. Very short-term wind direction prediction via self-tuning wavelet long-short term memory neural network[J]. Proceedings of the CSEE, 2019, 39 (15): 4459- 4468.
20	唐振浩, 赵赓楠, 曹生现, 等. 一种基于数据解析的混合风向预测算法[J]. 太阳能学报, 2021, 42 (9): 349- 356.
	TANG Zhenhao, ZHAO Gengnan, CAO Shengxian, et al. A data analystic based hybrid wind direction prediction algorithm[J]. Acta Energiae Solaris Sinica, 2021, 42 (9): 349- 356.
21	贾睿, 杨国华, 郑豪丰, 等. 基于自适应权重的CNN-LSTM&GRU组合风电功率预测方法[J]. 中国电力, 2022, 55 (5): 47- 56, 110.
	JIA Rui, YANG Guohua, ZHENG Haofeng, et al. Combined wind power prediction method based on CNN-LSTM & GRU with adaptive weights[J]. Electric Power, 2022, 55 (5): 47- 56, 110.
22	高阳, 谢丽蓉, 叶家豪, 等. 自适应提升及预测误差修正的风电功率超短期预测[J]. 智慧电力, 2022, 50 (8): 14- 21. DOI
	GAO Yang, XIE Lirong, YE Jiahao, et al. Ultra-short-term wind power prediction based on adaptive lifting and prediction error correction[J]. Smart Power, 2022, 50 (8): 14- 21. DOI
23	罗佑新, 何哲明, 车晓毅. 非线性方程组求解的超混沌序列最小二乘法及其应用[J]. 湖南文理学院学报(自然科学版), 2010, 22 (1): 36- 39.
	LUO Youxin, HE Zheming, CHE Xiaoyi. Hyper-chaotic sequence least square method for solving nonlinear equations and its application[J]. Journal of Hunan University of Arts and Science (Natural Science Edition), 2010, 22 (1): 36- 39.
24	IBRAHIM Y, WANG H B, LIU J Y, et al. Soft errors in DNN accelerators: a comprehensive review[J]. Microelectronics Reliability, 2020, 115, 113969. DOI
25	HAN H, XU L, CUI X Y, et al. Novel chiller fault diagnosis using deep neural network (DNN) with simulated annealing (SA)[J]. International Journal of Refrigeration, 2021, 121, 269- 278. DOI

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