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
Offshore Wind Farm Wake Deflection Control Based on Adaptive Wind Condition Prediction Error
阎洁 1杨佳琳 1王航宇 1卢姣阳 1刘永前 1张磊2
作者信息
- 1. 华北电力大学新能源电力系统国家重点实验室,北京 102206
- 2. 西藏自治区地勘局地质调查院,西藏拉萨 850000
- 折叠
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%.
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.
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Key words
offshore wind farm/wake control/yaw angle/wind condition prediction/error adaptation/deep neural network引用本文复制引用
基金项目
国家重点研发计划(2019YFE0104800)
出版年
2024
国家科技期刊平台
NSTL
万方数据