SCADA data based realistic simulation framework to evaluate environmental impact on performance of wind turbine condition monitoring systems

被引:0
|
作者
Aziz, Usama [1 ,2 ]
Charbonnier, Sylvie [1 ]
Berenguer, Christophe [1 ]
Lebranchu, Alexis [2 ]
Prevost, Frederic [2 ]
机构
[1] Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, F-38000 Grenoble, France
[2] Valemo SAS, F-33323 Begles, France
关键词
MODELS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Wind turbines are an integral part of renewable energy based power generation targets all over the world. With expanding fleets, varying geographical locations, improving technologies, competitive markets and a variety of manufactures to choose from, wind farm operators often end up with heterogeneous fleets. This paper presents a methodology to comprehensively evaluate the performance of fault indicators for wind turbines condition monitoring using power curves. A comprehensive analysis is done by taking into account various factors that can influence performance indicators such as the differences between the geographical locations of the wind farms and multiple fault signatures. A controlled and realistic simulation framework is presented and used to create a database covering multiple wind farms from different geographical conditions having distinct environmental profiles. Realistic fault signatures are used for condition monitoring and performance analysis using two fault detection approaches. Results show a trend in performance indicators associated to the distinct environmental profiles of the wind farms under observation. The results also highlight the limitations of local and site specific detection and performance analysis approaches and identify the distinct environmental profiles as key phenomenon for consideration in performance evaluation of fault detection methods.
引用
收藏
页码:360 / 365
页数:6
相关论文
共 50 条
  • [41] Wind Turbine Failure Prediction Model using SCADA-based Condition Monitoring System
    Alzawaideh, Bara
    Baboli, Payam Teimourzadeh
    Babazadeh, Davood
    Horodyvskyy, Susanne
    Koprek, Isabel
    Lehnhoff, Sebastian
    2021 IEEE MADRID POWERTECH, 2021,
  • [42] Condition Monitoring of Wind Turbine Main Bearing Using SCADA Data and Informed by the Principle of Energy Conservation
    de Oliveira, Adaiton Moreira
    Cambron, Philippe
    Tahan, Antoine
    2022 PROGNOSTICS AND HEALTH MANAGEMENT CONFERENCE, PHM-LONDON 2022, 2022, : 276 - 282
  • [43] CONDITION MONITORING WITH WIND TURBINE SCADA DATA USING NEURO-FUZZY NORMAL BEHAVIOR MODELS
    Schlechtingen, Meik
    Santos, Ilmar Ferreira
    PROCEEDINGS OF THE ASME TURBO EXPO 2012, VOL 6, 2012, : 717 - 726
  • [44] Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data
    McKinnon, Conor
    Carroll, James
    McDonald, Alasdair
    Koukoura, Sofia
    Infield, David
    Soraghan, Conaill
    ENERGIES, 2020, 13 (19)
  • [45] Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms
    Chesterman, Xavier
    Verstraeten, Timothy
    Daems, Pieter-Jan
    Nowe, Ann
    Helsen, Jan
    WIND ENERGY SCIENCE, 2023, 8 (06) : 893 - 924
  • [46] Calculation and Analysis of Wind Turbine Health Monitoring Indicators Based on the Relationships with SCADA Data
    Zhang, Fan
    Wen, Zejun
    Liu, Deshun
    Jiao, Jie
    Wan, Hengzheng
    Zeng, Bing
    APPLIED SCIENCES-BASEL, 2020, 10 (01):
  • [47] SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System
    Chen, Huanguo
    Xie, Chao
    Dai, Juchuan
    Cen, Enjie
    Li, Jianmin
    ENERGIES, 2021, 14 (21)
  • [48] Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples
    Schlechtingen, Meik
    Santos, Ilmar Ferreira
    APPLIED SOFT COMPUTING, 2014, 14 : 447 - 460
  • [49] Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description
    Schlechtingen, Meik
    Santos, Ilmar Ferreira
    Achiche, Sofiane
    APPLIED SOFT COMPUTING, 2013, 13 (01) : 259 - 270
  • [50] A Multi-View Spatio-Temporal Feature Fusion Approach for Wind Turbine Condition Monitoring Based on SCADA Data
    Wang, Hong
    Xie, Hui
    Liu, Shuwei
    Song, Songsong
    Han, Wei
    IEEE ACCESS, 2024, 12 : 43948 - 43957