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International Society of Science and Applied Technologies |
| Multi-Source Driven Opportunistic Group Maintenance Framework for Wind Turbines | ||||
| Author |
Yi Chen
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| Co-Author(s) |
Xiaobing Ma
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| Abstract | Wind turbines are critical assets in renewable energy systems, yet their operation faces challenges from complex components, variable environments, and high maintenance costs. Traditional maintenance strategies, often based on fixed schedules or isolated condition monitoring, fail to fully leverage the rich data from SCADA systems, sensors, and environmental measurements. This paper proposes a Multi‑Source Driven Opportunistic Group Maintenance (MS‑OGM) Framework that integrates heterogeneous data to enable dynamic, group-based maintenance across a fleet of turbines. By combining real-time degradation indicators and environmental conditions, the framework identifies opportunistic maintenance windows where multiple turbines or components can be serviced collectively, reducing downtime and resource use. A hierarchical decision model balances failure risk, maintenance cost, and energy production loss, while a coordination mechanism clusters turbines with aligned maintenance opportunities. Validation on simulated wind field data demonstrates that MS‑OGM improves reliability, reduces operational costs, and enhances maintenance efficiency compared to conventional approaches.
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| Keywords | wind turbine maintenance, multi-source driven framework, opportunistic group maintenance, predictive maintenance | |||
| Article #: RQD2026-252 | ||||
Proceedings of 31st ISSAT International Conference on Reliability & Quality in Design |