基于风况预测误差自适应的海上风电场尾流偏转控制方法

doi:10.11930/j.issn.1004-9649.202303020

摘要/Abstract

摘要：

风电场尾流偏转控制是降低尾流效应、提升整场发电量的重要手段。风况预测值是风电场尾流偏转控制的重要输入，其误差给实际控制效果带来巨大影响，甚至导致全场发电量“不增反降”，极大限制了风电场尾流偏转控制技术的工程应用。以某海上风电场实际运行数据为例，探索了分钟级风速和风向预测误差对风电场尾流偏转控制效果的影响，提出了风况预测误差自适应的海上风电场尾流偏转控制方法及基于深度神经网络的控制模型。研究结果表明：与不考虑风况预测误差自适应的传统风电场尾流偏转控制方法相比，所提方法的全场发电量提高了1.77%。

关键词: 海上风电, 尾流控制, 偏航角, 风况预测, 误差自适应, 深度神经网络

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

阎洁, 杨佳琳, 王航宇, 卢姣阳, 刘永前, 张磊. 基于风况预测误差自适应的海上风电场尾流偏转控制方法[J]. 中国电力, 2024, 57(3): 190-196.

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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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303020

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

图/表 10

表 1 场景设置

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

图 1 风速预测误差对控制效果的影响（非主导风向）

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

图 2 风速预测误差对控制效果的影响（主导风向）

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

图 3 风向预测误差对控制效果的影响（非主导风向）

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

图 4 风向预测误差对控制效果的影响（主导风向）

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

图 5 控制策略框架

Fig.5 Control strategy framework

图 6 神经元节点

Fig.6 Neuron node

图 7 DNN模型结构

Fig.7 DNN model structure

图 8 功率提升率对比

Fig.8 Comparison of power increase rate

图 9 测试集风况下改进模型与模型1的功率提升率

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

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