改进的风功率异常数据贝叶斯变点-Thompson tau清洗方法

王智明; 陈小国; 王领军; 鲁文彬

doi:10.19912/j.0254-0096.tynxb.2023-1973

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改进的风功率异常数据贝叶斯变点-Thompson tau清洗方法

更新时间：2025-10-18

- 改进的风功率异常数据贝叶斯变点-Thompson tau清洗方法
- IMPROVED BAYESIAN CHANGE POINT-THOMPSON TAU CLEANING METHOD FOR WIND POWER ANOMALIES
- 太阳能学报 2025年46卷第3期页码：687-696
- 作者机构：
  
  兰州理工大学机电工程学院,兰州 730050
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2023-1973
  中图分类号： TK83TM619
- 网络出版：2025-04-07，
  
  纸质出版：2025
- 稿件说明：
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王智明, 陈小国, 王领军, 鲁文彬. 改进的风功率异常数据贝叶斯变点-Thompson tau清洗方法[J]. 太阳能学报, 2025, 46(3): 687-696.

Wang Zhiming, Chen Xiaoguo, Wang Lingjun, et al. IMPROVED BAYESIAN CHANGE POINT-THOMPSON TAU CLEANING METHOD FOR WIND POWER ANOMALIES[J]. 2025, 46(3): 687-696.
王智明, 陈小国, 王领军, 鲁文彬. 改进的风功率异常数据贝叶斯变点-Thompson tau清洗方法[J]. 太阳能学报, 2025, 46(3): 687-696. DOI： 10.19912/j.0254-0096.tynxb.2023-1973.

Wang Zhiming, Chen Xiaoguo, Wang Lingjun, et al. IMPROVED BAYESIAN CHANGE POINT-THOMPSON TAU CLEANING METHOD FOR WIND POWER ANOMALIES[J]. 2025, 46(3): 687-696. DOI： 10.19912/j.0254-0096.tynxb.2023-1973.

摘要

根据风功率异常数据分布特点及其产生原因提出一种改进贝叶斯变点-Thompson tau异常数据清洗方法。首先

对小于切入风速的非零功率值进行剔除;接着基于改进贝叶斯变点-Thompson tau法剔除功率曲线底部堆积和周围离散型异常值

得到清洗后的正常数据;最后

应用该文所提方法

对某风场6台机组的实际功率风速数据进行清洗

用清洗时间、数据删除率、均方根误差及决定系数等评价指标对所提方法进行验证。分析结果显示：与四分位-k-均值聚类算法、最优组内方差算法、贝叶斯变点-四分位法及Thompson tau-四分位法比较

该文方法能有效识别和剔除各类功率风速异常值

所建功率曲线精度高

且清洗时间短

清洗效果好

通用性较强。

Abstract

According to the distribution characteristics and causes of abnormal wind power data

an improved Bayesian change point-Thompson tau abnormal data cleaning method is proposed. First

the non-zero power value less than the cut-in wind speed is eliminated. Then

abnormal values accumulated at the bottom of the power curve and the surrounding discrete outliers are removed based on the improved Bayesian change point-Thompson tau method

and the cleaned normal data are obtained. Finally

the proposed method is applied to clean the actual power wind speed data of six units in a wind field

and the evaluation indexes such as cleaning time

data deletion rate

root mean square error and determination coefficient are used to verify the proposed method. The results show that compared with quartile k-means clustering algorithm

optimal intra-group variance algorithm

Bayesian change point-quartile method and Thompson tau-quartile method

the proposed method can effectively identify and eliminate all kinds of power and wind speed outliers

and the constructed power curve has high accuracy. The cleaning time is short

cleaning effect is good and versatility is strong.

关键词

Keywords

references

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