机器学习通过智能故障检测和战略定位来提高风力轮机的效率
Sekar Kidambi Raju1, Muthusamy Periyasamy2, Amel Ali Alhussan3
1School of Computing, SASTRA Deemed University, Thanjavur, 613401, India. sekar1971kr@gmail.com.
Scientific reports
|January 9, 2025
概括
本研究介绍了HARO模型用于智能风力轮机健康监测,提高故障预测准确度和减少停机时间. 这种机器学习方法提高了风能可靠性,以实现可持续的未来.
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
- 机械工程 机械工程
背景情况:
- 风力轮机健康监测 (WTHM) 传统上依赖于振动分析或SCADA数据,通常涉及耗时的手动故障识别.
- 风力轮机中故障模式识别的传统方法是低效的,易于猜测.
- 提高风能效率和可靠性对于可持续能源未来至关重要.
研究的目的:
- 开发一种智能自动化方法,用于风力轮机的早期故障检测.
- 通过机器学习提高风力轮机健康监测 (WTHM) 的准确性和效率.
- 为了减少运营停机时间,并优化风力轮机的维护计划.
主要方法:
- 实施拟议的HARO (Huber Adam回归优化器) 模型,将变压器网络与拉索回归和Adam优化器集成.
- 利用由变压器网络学习的传感器数据模式,以改进故障检测.
- 将HARO模型与传统的回归器 (如Huber和自动相关性确定 (ARD)) 进行比较.
主要成果:
- 哈罗模型证明了在预测未来风力轮机故障方面减少了停机时间和提高了准确性.
- 自动化方法最大限度地减少了人类的投入,从而使维护计划更有效.
- 该研究证实了机器学习在提高风力轮机可靠性的潜力.
结论:
- 在风力轮机故障检测的传统方法上,HARO模型提供了显著的进步.
- 准确的故障预测可以及时维护,提高轮机的整体效率和可靠性.
- 机器学习是建立风能作为全球可再生能源系统可靠组成部分的关键.
- 集体研究工作对于应对风力发电维护方面的挑战和推动持续改进至关重要.
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