Cleaning and modeling of abnormal data of wind farm power curve

Abstract

Abstract: The wind speed and power data of wind farms usually contain a large number of abnormal data, which is difficult to reflect the real working conditions of wind turbines, affect the accuracy of wind power prediction, and then cause certain economic losses. To solve this problem, the characteristics of abnormal data are analyzed, a slip-quartile abnormal data elimination method is proposed, and the wind speed power curve of the eliminated data is modeled by using high-order polynomial and Logistic function. Finally, the applicability and effectiveness of this method are verified by variance, root means square error and determination coefficient. The analysis in the example shows that the method is simple, efficient and versatile, and can significantly improve the accuracy of wind turbine power characteristic analysis.

Key words: wind power curve, data cleaning, abnormal data classification, slip-quartile algorithm

CLC Number:

TK83
TM619

CAO Li-xin, LIU Wei-min, GUO Hu-quan. Cleaning and modeling of abnormal data of wind farm power curve[J]. Journal of Lanzhou University of Technology, 2022, 48(4): 64-70.

References

[1] Global Wind Energy Council (GWEC).Global wind report 2021 [EB/OL].[2021-4-21].https://gwec.Net/global-wind-report-2021/.html.
[2] ZAHER A,MCARTHUR S D J,INFIELD D G,et al.Online wind turbine fault detection through automated SCADA data analysis [J].Wind Energy, 2009, 12(6):574-593.
[3] DIAZ H,GUEDES SOARES C.Review of the current status,technology and future trends of offshore wind farms [J].Ocean Engineering,2020,209:107381.
[4] LIMA D C A,SOARES P M M,CARDOSO R M,et al.The present and future offshore wind resource in the Southwestern African region [J].Climate Dynamics,2021,56(5):1371-1388.
[5] 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.
[6] HU Y,XI Y H,PAN C Y,et al.Daily condition monitoring of grid-connected wind turbine via high-fidelity power curve and its comprehensive rating [J].Renewable Energy,2020,146:2095-2111.
[7] PARK J Y,LEE J K,OH K Y,et al.Development of a novel power curve monitoring method for wind turbines and its field tests [J].IEEE Transactions on Energy Conversion,2014,29(1):119-128.
[8] 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.
[9] 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,2018,10(1):46-54.
[10] 赵永宁,叶林,朱倩雯,等.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.
[11] 朱倩雯,叶林,赵永宁,等.风电场输出功率异常数据识别与重构方法研究 [J].电力系统保护与控制,2015,43(3):38-45.
[12] ZHENG L,HU W,MIN Y.Raw wind data preprocessing:A data-mining approach [J].IEEE Transactions on Sustainable Energy,2015,6(1):11-19.
[13] 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.
[14] 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.
[15] WANG Y,INFIELD D G,STEPHEN B,et al.Copula-based model for wind turbine power curve outlier rejection [J].Wind Energy,2014,17(11):1677-1688.
[16] GILL S,STEPHEN B,GALLOWAY S.Wind turbine condition assessment through power curve copula modeling [J].IEEE Transactions on Sustainable Energy,2012,3(1):94-101.
[17] 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.
[18] KUSIAK A,ZHENG H Y,SONG Z.Models for monitoring wind farm power [J].Renewable Energy,2009,34(3):583-590.
[19] ASTOLFI D,BYRNE R,CASTELLANI F.Analysis of wind turbine aging through operation curves [J].Energies,2020,13(21):5623.
[20] MARCIUKAITIS M,ŽUTAUTAITE I,MARTISAUSKAS L,et al.Non-linear regression model for wind turbine power curve [J].Renewable Energy,2017,113:732-741.
[21] CARRILLO C,OBANDO MONTANO A F,CIDRÁS J,et al.Review of power curve modelling for wind turbines [J].Renewable and Sustainable Energy Reviews,2013,21:572-581.
[22] THAPAR V,AGNIHOTRI G,SETHI V K.Critical analysis of methods for mathematical modelling of wind turbines [J].Renewable Energy,2011,36(11):3166-3177.
[23] TASLIMI-RENANI E,MODIRI-DELSHAD M,ELIAS M F M,et al.Development of an enhanced parametric model for wind turbine power curve [J].Applied Energy,2016,177:544-552.
[24] KUSIAK A,ZHENG H Y,SONG Z.Models for monitoring wind farm power [J].Renewable Energy,2009,34(3):583-590.
[25] 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.
[26] VILLANUEVA D,FEIJO O A E.Reformulation of parameters of the logistic function applied to power curves of wind turbines [J].Electric Power Systems Research,2016,137:51-58.
[27] MANOBEL B,SEHNKE F,LAZZUS J A,et al.Wind turbine power curve modeling based on Gaussian processes and artificial neural networks [J].Renewable Energy,2018,125:1015-1020.
[28] 杨茂,张罗宾,崔杨,等.基于混合半云模型的风速-功率曲线建模方法 [J].电力自动化设备,2020,313(5):142-150.
[29] Wind energy generation systems-Part 12-1:Power performance measurements of electricity producing wind turbines:IEC 61400-12-1:2017 [S].Geneva:The International Electrotechnical Commission (IEC),2017:42-65.