An artificial intelligence wind power equipment operation and maintenance system and method using big data

With the continuous exploitation of fossil fuels, the reserves of this non-renewable energy source are increasingly being consumed. To alleviate the energy crisis and the environmental problems caused by fossil energy, wind power is now forming a boom in the world. However, the distribution of wind turbine units is relatively scattered, with each unit being far apart. Moreover, the cabins of wind turbines are mostly located at a height of several tens of metres, making traditional manual maintenance very difficult. The Artificial Intelligence (AI) wind power equipment Operation and Maintenance (O&M) system can display various technical indicators of the operation of each generator unit in real-time through Big Data (BD) technology, which plays an essential and positive role in reducing the O&M risks of O&M personnel and improving O&M efficiency. This article studied the O&M system and methods of wind power equipment using BD AI technology. The final experimental results showed that the wind power equipment O&M system using BD AI technology had an average maintenance difficulty score of 95.817 points, average maintenance duration of 4.208 hours and an average maintenance cost of 146,300 US dollars, which had significant advantages compared to traditional manual maintenance.

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Publication Details

Journal
International Journal of Global Energy Issues
Published
2025-12-23
DOI
https://doi.org/10.1504/ijgei.2026.150723
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
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article

An artificial intelligence wind power equipment operation and maintenance system and method using big data

Zhishuai Zheng, Chaodong Wang, Tao Feng
International Journal of Global Energy Issues
Machine Fault Diagnosis Techniques
article

An artificial intelligence wind power equipment operation and maintenance system and method using big data

Zhishuai Zheng, Chaodong Wang, Tao Feng
article en

Abstract

With the continuous exploitation of fossil fuels, the reserves of this non-renewable energy source are increasingly being consumed. To alleviate the energy crisis and the environmental problems caused by fossil energy, wind power is now forming a boom in the world. However, the distribution of wind turbine units is relatively scattered, with each unit being far apart. Moreover, the cabins of wind turbines are mostly located at a height of several tens of metres, making traditional manual maintenance very difficult. The Artificial Intelligence (AI) wind power equipment Operation and Maintenance (O&M) system can display various technical indicators of the operation of each generator unit in real-time through Big Data (BD) technology, which plays an essential and positive role in reducing the O&M risks of O&M personnel and improving O&M efficiency. This article studied the O&M system and methods of wind power equipment using BD AI technology. The final experimental results showed that the wind power equipment O&M system using BD AI technology had an average maintenance difficulty score of 95.817 points, average maintenance duration of 4.208 hours and an average maintenance cost of 146,300 US dollars, which had significant advantages compared to traditional manual maintenance.

International Journal of Global Energy IssuesVol. 48(1/2)
Xuchang University (CN)
Affordable and clean energy
Openalex Percentile: Top 38%
Machine Fault Diagnosis Techniques
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An artificial intelligence wind power equipment operation and maintenance system and method using big data — Zhishuai Zheng, Chaodong Wang, et al. · International Journal of Global Energy Issues (2025) | TGRS Research Map | TGRS