Your Location:
Home >
Browse articles >
Research on Fault Diagnosis Method of Photovoltaic Arrays Based on Improved Grey Wolf Algorithm Optimized Extreme Learning Machine
Power Generation Technology   Vol. 46, Issue 1, Pages: 72-82(2025)
New Energy|Updated:2026-06-29
  • Export

  • Collection

  • Album

    • Research on Fault Diagnosis Method of Photovoltaic Arrays Based on Improved Grey Wolf Algorithm Optimized Extreme Learning Machine

    • “The fault diagnosis of photovoltaic arrays faces complex working conditions challenges. A fault diagnosis method based on improved grey wolf algorithm optimized extreme learning machine is proposed. Researchers establish a 5-dimensional fault feature vector and improve the optimization algorithm, with a classification accuracy of 98.32%, providing an efficient solution for photovoltaic system state monitoring.”
    • DOI:10.12096/j.2096-4528.pgt.23117    

      CLC:
    • Revised:2024-03-01,

    Scan QR Code

  •  
  •  

86

Views

403

Downloads

3

CSCD

Alert me when the article has been cited
Submit
Outline

Related Articles

Fault Diagnosis Method for Hydroturbine Units Based on Transformer and Multimodal Heterogeneous Feature Fusion
Bearing Faults Diagnosis Method Based on Stacked Auto-Encoder With Graph Regularization for Wind Turbines
Research Progress of Vibration Fault Diagnosis Technology for Steam Turbine Generator Sets
Application and Challenges of Large Models in Power Industry
State Estimation and Fault Diagnosis of Proton Exchange Membrane Fuel Cells Based on Artificial Intelligence

Related Author

MA Chengwei
FANG Xiansi
LÜ Shunli
HE Yuping
YOU Wanfang
MIN Wanxiong
LI Junsong
LIU Jiajia

Related Institution

Nanjing Nanrui Water Conservancy and Hydropower Technology Co., Ltd.
Goupitan Power Plant, Guizhou Wujiang Hydropower Development Co., Ltd.
School of Metallurgical and Energy Engineering, Kunming University of Science and Technology
Beijing Pukang Measurement and Monitoring Tech Co., Ltd., Fengtai District
College of Information Science and Technology, Beijing University of Chemical Technology, Chaoyang District