基于堆叠集成算法和自适应值的风力轮发电机故障诊断
Zhanjun Tang1, Xiaobing Shi1, Huayu Zou1,2
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究引入了适应值方法用于风力轮发电机故障诊断,显著减少警报时间延迟. 新的机器学习模型提高了精度,降低了风能系统的运营成本.
科学领域:
- 可再生能源系统可再生能源系统
- 机器学习应用 机器学习应用
- 预测性维护是指预测性维护.
背景情况:
- 故障报警时间延迟阻碍了精确的风力轮发电机 (WTG) 诊断.
- 目前的方法不足以快速检测WTG故障,增加运营成本.
- 有效的故障诊断对于可靠的风能生产至关重要.
研究的目的:
- 为WTGs开发一种新的,快速和准确的故障诊断方法.
- 降低与WTG相关的运营和维护成本.
- 提高风力轮机运行的可靠性和效率.
主要方法:
- 使用LightGBM,XGBoost和SGDRegressor构建了一个堆叠组合模型.
- 采用贝叶斯调整用于自动超参数优化.
- 应用了Pauta标准 (3σ) 和适应值方法的时间滑动窗口与合的残余.
主要成果:
- 拟议的自适应值方法在故障诊断中表现优于固定值.
- 实现了显著更早的警报时间:GENERATOR故障1.5小时,GENERATOR_BEARING故障5.8小时,变压器故障3小时.
- 使用R2和RMSE指标证明了模型的准确性和适用性.
结论:
- 开发的自适应值方法可以更快,更准确地进行WTG故障诊断.
- 这种方法有效地解决了故障报警时间延迟的挑战.
- 这些发现有助于降低WTG的运营成本,提高能源系统的可靠性.
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