智能运维与预测模型优化提升风力发电效率

摘要

本研究探讨了预测性维护模型的有效性及智能运维（O&M）系统优化在提升风力发电效率方面的作用。通过定性研究，我们对五位拥有丰富风机运行经验的风电场工程师和维护经理进行了结构化访谈。采用主题分析法，研究发现，尽管预测性维护模型能有效识别重大故障以减少停机时间，但在检测较小、渐进性故障方面仍存在困难。识别出的主要挑战包括误报、传感器故障以及新模型与老旧风机系统集成的难题。数字孪生、SCADA系统和状态监测等先进技术显著提升了风机维护实践。然而，这些技术仍需改进，特别是在人工智能优化和实时数据集成方面。研究结果强调了持续开发的必要性，以充分优化风机性能并支持可再生能源的更广泛应用。

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.

智能运维与预测模型优化提升风力发电效率

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

摘要

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