提升風力發電效率的智能運維與預測模型優化

摘要

本研究探討了預測性維護模型的有效性及智能運維（O&M）系統的優化在提升風力發電效率方面的作用。通過質性研究，我們對五位具有豐富風機操作經驗的風電場工程師和維護經理進行了結構化訪談。採用主題分析法，研究發現，雖然預測性維護模型能有效識別重大故障並減少停機時間，但在檢測較小且漸進性故障方面仍存在困難。研究指出的主要挑戰包括誤報、感測器故障以及新模型與舊風機系統整合的困難。數位孿生、SCADA系統和狀態監測等先進技術已顯著提升了風機維護實踐。然而，這些技術仍需改進，特別是在AI精煉和即時數據整合方面。研究結果強調了持續發展的必要性，以充分優化風機性能並支持可再生能源的更廣泛應用。

English

This study explores the effectiveness of predictive maintenance models and the optimization of intelligent Operation and Maintenance (O&M) systems in improving wind power generation efficiency. Through qualitative research, structured interviews were conducted with five wind farm engineers and maintenance managers, each with extensive experience in turbine operations. Using thematic analysis, the study revealed that while predictive maintenance models effectively reduce downtime by identifying major faults, they often struggle with detecting smaller, gradual failures. Key challenges identified include false positives, sensor malfunctions, and difficulties in integrating new models with older turbine systems. Advanced technologies such as digital twins, SCADA systems, and condition monitoring have significantly enhanced turbine maintenance practices. However, these technologies still require improvements, particularly in AI refinement and real-time data integration. The findings emphasize the need for continuous development to fully optimize wind turbine performance and support the broader adoption of renewable energy.

提升風力發電效率的智能運維與預測模型優化

Intelligent Operation and Maintenance and Prediction Model Optimization for Improving Wind Power Generation Efficiency

摘要

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