A Self-Improved Optimizer-Based CNN for Wind Turbine Fault Detection

被引:3
|
作者
Ahilan, T. [1 ]
Narasimhulu, Andriya [2 ]
Prasad, D. V. S. S. S. V. [3 ]
机构
[1] St Joseph Coll Engn, Near Toll Plaza, Chennai, Tamil Nadu, India
[2] Netaji Subhas Univ Technol, Dept Mech Engn, Dwarka Sect 3, Delhi, India
[3] Aditya Coll Engn, Dept Mech Engn, Surampalem, Andhra Pradesh, India
关键词
SCADA; wind power plant; CNN; error analysis; fault detection; DIAGNOSIS METHOD;
D O I
10.1142/S021812662350247X
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In comparison to other alternative energy sources, wind power is more affordable and environmentally friendly, making it one of the most significant energy sources in the world. It is vital to monitor the condition of each wind turbine in the farm and recognize the various states of alert since difficulties with the operation as well as maintenance of wind farms considerably contribute to the rise in their overall expenses. The Supervisory Control and Data Acquisition (SCADA) data-based continuous observation of wind turbine conditions is the most widely used existing strategy to detect the fault early by preventing the wind turbine from reaching a shutdown stage. Several parameters irrelevant to the faults are saved in the SCADA system while the wind turbine is operating. To increase the efficacy of wind turbine fault diagnostics, optimally selected SCADA data parameters are required for fault prediction. Hence, this paper introduces an optimized Convolutional Neural Network (CNN)-based wind turbine fault identification method. For more precise detection, a Self-Improved Slime Mould Algorithm (SI-SMA) is used for the optimal selection of SCADA parameters as well as weight optimization of CNN. The proposed SI-SMA method is an enhanced form of the standard Slime Mould Algorithm (SMA). Eventually, an error analysis and a stability analysis are carried out to check the overall effectiveness of the suggested approach. In particular, the root mean square error (RMSE) of the implemented algorithm is lower, and it is 0.69%, 1.58%, 0.81% and 1.71% better than the existing FF, GWO, WOA and SMA models.
引用
收藏
页数:24
相关论文
共 50 条
  • [21] Wind turbine fault detection based on SCADA data analysis using ANN
    Zhang, Zhen-You
    Wang, Ke-Sheng
    ADVANCES IN MANUFACTURING, 2014, 2 (01) : 70 - 78
  • [22] Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data
    Vidal, Yolanda
    Pozo, Francesc
    Tutiven, Christian
    ENERGIES, 2018, 11 (11)
  • [23] A prognostic method for fault detection in wind turbine drivetrains
    Nejad, Amir Rasekhi
    Odgaard, Peter Fogh
    Gao, Zhen
    Moan, Torgeir
    ENGINEERING FAILURE ANALYSIS, 2014, 42 : 324 - 336
  • [24] Fault detection of key components of wind turbine based on combineation prediction model
    Su L.
    Xing M.
    Zhang H.
    Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2021, 42 (10): : 220 - 225
  • [25] Wind turbine fault detection based on SCADA data analysis using ANN
    Zhen-You Zhang
    Ke-Sheng Wang
    Advances in Manufacturing, 2014, 2 : 70 - 78
  • [26] WIND TURBINE FAULT DETECTION METHOD BASED ON DYNAMIC NEIGHBORHOOD INDEX RECONSTRUCTION
    Qian, Xiaoyi
    Sun, Tianhe
    Jang, Xingyu
    Wang, Baoshi
    Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2024, 45 (09): : 557 - 563
  • [27] Current-Based Fault Detection and Identification for Wind Turbine Drivetrain Gearboxes
    Cheng, Fangzhou
    Peng, Yayu
    Qu, Liyan
    Qiao, Wei
    IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2017, 53 (02) : 878 - 887
  • [28] Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data
    Velandia-Cardenas, Cristian
    Vidal, Yolanda
    Pozo, Francesc
    ENERGIES, 2021, 14 (06)
  • [29] Fault detection of a wind turbine generator bearing using interpretable machine learning
    Bindingsbo, Oliver Trygve
    Singh, Maneesh
    Ovsthus, Knut
    Keprate, Arvind
    FRONTIERS IN ENERGY RESEARCH, 2023, 11
  • [30] A Wind Turbine Fault Detection Approach Based on Cluster Analysis and Frequent Pattern Mining
    Elijorde, Frank
    Kim, Sungho
    Lee, Jaewan
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2014, 8 (02): : 664 - 677