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

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