An artificial intelligence wind power equipment operation and maintenance system and method using big data
Tao Feng et al.
What the paper says
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.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.