基于规则库与PRRL模型的风电功率数据清洗方法

杨海能; 唐杰; 邵武; 刘白杨; 陈日恒

doi:10.19912/j.0254-0096.tynxb.2024-0179

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (12) : 416-425. DOI: 10.19912/j.0254-0096.tynxb.2024-0179

基于规则库与PRRL模型的风电功率数据清洗方法

杨海能, 唐杰^1,2, 邵武², 刘白杨², 陈日恒²

作者信息 +

WIND POWER DATA CLEANING METHOD BASED ON RULE BASE AND PRRL MODEL

Yang Haineng, Tang Jie^1,2, Shao Wu², Liu Baiyang², Chen Riheng²

Author information +

文章历史 +

摘要

为提升风电场原始数据中异常数据的识别精度,提出一种结合规则库与PRRL模型的风电场数据清洗方法。首先依据风电场装机容量等参数建立规则库,提高数据集中正常数据占比。其次,以RANSAC稳健回归算法为核心,线性回归模型为基础,将风速数据作为输出,同时扩展输入变量的多项式特征来捕捉风速与功率之间的非线性关系,构建PRRL稳健回归模型。该模型经过规则库处理后的数据训练,可有效识别原始数据中的异常数据。通过对湖南某风电场的实例数据进行应用测试,结果显示该方法在处理异常数据占比较高数据时,能有效识别其中的异常数据,并降低风电功率预测模型的预测误差。

Abstract

In order to improve the identification accuracy of abnormal data in the original data of wind farms, a wind farm data cleaning method combining a rule base and the PRRL model is proposed. First, a rule base is established based on parameters such as the installed capacity of the wind farm to increase the proportion of normal data in the dataset. Second, the PRRL robust regression model is constructed using the RANSAC robust regression algorithm as the core and the linear regression model as the foundation, taking wind speed data as the output and expanding the polynomial features of input variables to capture the nonlinear relationship between wind speed and power. The model is trained on data processed by the rule base, effectively identifying abnormal data in the original dataset. Application testing using sample data from a wind farm in Hunan shows that this method can effectively identify abnormal data when processing the data with a high proportion of abnormal data, and reduce the prediction error of the wind power forecasting model.

导出引用

杨海能, 唐杰, 邵武, 刘白杨, 陈日恒. 基于规则库与PRRL模型的风电功率数据清洗方法[J]. 太阳能学报. 2024, 45(12): 416-425 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0179

Yang Haineng, Tang Jie, Shao Wu, Liu Baiyang, Chen Riheng. WIND POWER DATA CLEANING METHOD BASED ON RULE BASE AND PRRL MODEL[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 416-425 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0179

中图分类号： TK81

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