IMPROVED BAYESIAN CHANGE POINT-THOMPSON TAU CLEANING METHOD FOR WIND POWER ANOMALIES

Wang Zhiming; Chen Xiaoguo; Wang Lingjun; Lu Wenbin

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

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

IMPROVED BAYESIAN CHANGE POINT-THOMPSON TAU CLEANING METHOD FOR WIND POWER ANOMALIES

更新时间：2025-10-18

- IMPROVED BAYESIAN CHANGE POINT-THOMPSON TAU CLEANING METHOD FOR WIND POWER ANOMALIES
- Vol. 46, Issue 3, Pages: 687-696(2025)
- 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
  CLC： TK83TM619
- Published Online：07 April 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
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

任大伟, 肖晋宇, 侯金鸣, 等. 双碳目标下我国新型电力系统的构建与演变研究[J]. 电网技术, 2022, 46(10): 3831-3839.

董海鹰, 陈彦求, 汪宁渤, 等. 含前端调速式风电机组风电场的无功运行优化[J]. 太阳能学报, 2020, 41(11): 254-260.

WU J H, SHAO Z G, YANG S H.A combined algorithm for data cleaning of wind power scatter diagram considering actual engineering characteristics[J]. Journal of physics: conference series, 2020, 1639(1): 012044.

刘明宇, 姚芳, 李志刚, 等. 风电机组功率模型参数辨识及风资源利用率评估[J]. 太阳能学报, 2020, 41(12): 305-315.

林鹏, 赵书强, 谢宇琪, 等. 基于实测数据的风电功率曲线建模及不确定估计[J]. 电力自动化设备, 2015, 35(4): 90-95.

KUSIAK A, ZHENG H Y, SONG Z.Models for monitoring wind farm power[J]. Renewable energy, 2009, 34(3): 583-590.

尚海勇, 刘利强, 齐咏生, 等. 基于数字孪生技术的风电机组建模研究[J]. 太阳能学报, 2023, 44(5): 391-400.

杨茂, 杨琼琼. 风电机组风速-功率特性曲线建模研究综述[J]. 电力自动化设备, 2018, 38(2): 34-43.

YIN X Q, LIU Y, YANG L, et al.Abnormal data cleaning method for wind turbines based on constrained curve fitting[J]. Energies, 2022, 15(17): 6373.

李阳, 沈小军, 张扬帆, 等. 基于速度-关联约束的风电机组风速感知异常数据识别方法[J]. 电工技术学报, 2023, 38(7): 1793-1807.

ZHAO Y N, YE L, WANG W S, et al.Data-driven correction approach to refine power curve of wind farm under wind curtailment[J]. IEEE transactions on sustainable energy, 2018, 9(1): 95-105.

赵永宁, 叶林, 朱倩雯. 风电场弃风异常数据簇的特征及处理方法[J]. 电力系统自动化, 2014, 38(21): 39-46.

陈伟, 吴布托, 裴喜平. 风电机组异常数据预处理的分类多模型算法[J]. 电力系统及其自动化学报, 2018, 30(4): 137-143.

胡阳, 乔依林. 基于置信等效边界模型的风功率数据清洗方法[J]. 电力系统自动化, 2018, 42(15): 18-23, 149.

SUN Q L, LIU C L, ZHEN C G.Abnormal detection of wind turbine operating conditions based on state curves[J]. Journal of energy engineering, 2019, 145(5): 06019001.

娄建楼, 胥佳, 陆恒, 等. 基于功率曲线的风电机组数据清洗算法[J]. 电力系统自动化, 2016, 40(10): 116-121.

SHEN X J, FU X J, ZHOU C C.A combined algorithm for cleaning abnormal data of wind turbine power curve based on change point grouping algorithm and quartile algorithm[J]. IEEE transactions on sustainable energy, 2019, 10(1): 46-54.

LONG H, SANG L W, WU Z J, et al.Image-based abnormal data detection and cleaning algorithm via wind power curve[J]. IEEE transactions on sustainable energy, 2020, 11(2): 938-946.

王新, 王政霞. 基于改进bin算法的风电机组风速-功率数据清洗[J]. 智能科学与技术学报, 2020, 2(1): 62-71.

YE X, LU Z X, QIAO Y, et al.Identification and correction of outliers in wind farm time series power data[J]. IEEE transactions on power systems, 2016, 31(6): 4197-4205.

吴永斌, 张建忠, 袁正舾, 等. 风电场风功率异常数据识别与清洗研究综述[J]. 电网技术, 2023, 47(6): 2367-2380.

齐培艳, 田铮, 段西发, 等. 异方差非参数回归模型均值与方差变点的小波估计与应用[J]. 系统工程理论与实践, 2013, 33(4): 988-995.

周影辉, 倪中新, 杨爱军. 0-1序列中变点的贝叶斯分析[J]. 数理统计与管理, 2014, 33(6): 1001-1009.

WANG Z M, LIU W M, WANG X.Abnormal data cleaning of wind turbine power curve using Bayesian change point-quartile combined algorithm[J]. Proceedings of the institution of mechanical engineers, part A: journal of power and energy, 2023, 237(3): 495-503.

YAO Q T, ZHU H W, XIANG L, et al.A novel composed method of cleaning anomy data for improving state prediction of wind turbine[J]. Renewable energy, 2023, 204: 131-140.

邹同华, 高云鹏, 伊慧娟, 等. 基于Thompson tau-四分位和多点插值的风电功率异常数据处理[J]. 电力系统自动化, 2020, 44(15): 156-162.

LYDIA M, SELVAKUMAR A I, KUMAR S S, et al.Advanced algorithms for wind turbine power curve modeling[J]. IEEE transactions on sustainable energy, 2013, 4(3): 827-835.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

SECONDARY FREQUENCY REGULATION CONTROL OF WIND FARM BASED ON GENREALIZED PREDICTIVE CONTROL

Related Author

Liu Yingming

Wang Shuqi

Wang Xiaodong

李元正

崔权维

周建星

温建民

费翔

Related Institution

School of Electrical Engineering, Shenyang University of Technology

Mingyang Smart Energy Group

Hainan Mingyang Smart Energy Co., Ltd.

School of Mechanical Engineering, Shenyang University of Technology

School of Electrical Engineering and Information Engineering, Lanzhou University of Technology

AI问答

⁰