基于尾流效应与机器学习的风功率预测研究

doi:10.3969/j.issn.1008-0198.2023.03.008

湖南电力 ›› 2023, Vol. 43 ›› Issue (3): 53-59.doi: 10.3969/j.issn.1008-0198.2023.03.008

基于尾流效应与机器学习的风功率预测研究

余忠泽¹, 刘震卿¹, 马驰²

1.华中科技大学土木与水利工程学院,湖北武汉 430074;
2.中国广核新能源控股有限公司,北京100084

收稿日期:2023-04-13 出版日期:2023-06-25 发布日期:2023-06-25
通信作者: 刘震卿(1984),男,教授,主要研究方向为风资源评估、风力发电机振动控制、海上漂浮式风机、风浪能联合利用、龙卷风等特异风灾。
作者简介:余忠泽(2001),男,主要研究方向为短期风功率预测。马驰(1980),男,工程师,主要研究方向为风力发电生产运维。
基金资助:
国家自然科学基金面上项目(51978307)

Study on Wind Power Prediction Based on Wake Effect and Machine Learning

YU Zhongze¹, LIU Zhenqing¹, MA Chi²

1. School of Civil and Hydraulic Engineering, Huazhong University of Science & Technology, Wuhan 430074, China;
2. China General Nuclear Power New Energy Holding Company,Beijing 100084, China

Received:2023-04-13 Online:2023-06-25 Published:2023-06-25

摘要/Abstract

摘要： 针对采用Jensen尾流解析模型分析尾流效应时,未考虑上游风机所带来的附加湍流强度对下游风速恢复的影响,从而导致严重高估尾流区风速损失的问题,提出采用高斯尾流模型考虑尾流效应,并与机器学习算法相结合,对现有基于物理效应启发的短期风功率预测方法进行改进,使得短期风功率预测模型在尾流区预估风速精度方面从理论上得到提高。以真实风电场历史风电数据开展实验,结果表明,与不考虑尾流效应相比,模型预测性能在均方误差方面降低约15%;与基于Jensen尾流解析模型的预测方法相比,模型预测性能在均方误差方面降低约7%。

关键词: 尾流效应, 机器学习, 风功率预测, 高斯尾流解析模型

Abstract: In response to the problem of severely overestimating wind speed loss in the wake zone when using the Jensen wake analysis model to analyze wake effects without considering the additional turbulence intensity brought by upstream fans on downstream wind speed recovery, a Gaussian wake model is proposed to consider wake effects and combine it with machine learning algorithms to improve existing short-term wind power prediction methods based on physical effects. This theoretically improves the accuracy of short-term wind power prediction models in predicting wind speed in the wake region. Experiments are conducted using historical wind power data from real wind farms, and the results show that compared to not considering wake effects, the model's prediction performance had a mean square error reduction of about 15%,and compared with the Jensen wake analysis model, the model's prediction performance had a mean square error reduction of about 7%.

Key words: wake effect, machine learning, wind power prediction, Gaussian wake analysis model

中图分类号:

TM614

余忠泽, 刘震卿, 马驰. 基于尾流效应与机器学习的风功率预测研究[J]. 湖南电力, 2023, 43(3): 53-59.

YU Zhongze, LIU Zhenqing, MA Chi. Study on Wind Power Prediction Based on Wake Effect and Machine Learning[J]. Hunan Electric Power, 2023, 43(3): 53-59.

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基于尾流效应与机器学习的风功率预测研究

Study on Wind Power Prediction Based on Wake Effect and Machine Learning

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