使用基于 LSTM 的堆叠去噪自动编码器网络和堆叠算法进行风力涡轮发电机早期故障诊断

IF 3.1 4区工程技术 Q3 ENERGY & FUELS

International Journal of Green Energy Pub Date : 2024-02-15 DOI:10.1080/15435075.2024.2315445

Junshuai Yan, Yongqian Liu, Hang Meng, Li Li, Xiaoying Ren

引用次数: 0

摘要

为减少风力涡轮发电机故障恶化造成的重大经济损失，迫切需要检测和诊断发电机的早期故障。风力发电机组现有的状态监测和故障诊断技术可以帮助我们了解风力发电机组的运行状况。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Wind turbine generator early fault diagnosis using LSTM-based stacked denoising autoencoder network and stacking algorithm

To reduce the significant economic losses caused by the fault deterioration of wind turbine generators, it is urgent to detect and diagnose the early faults of generators. The existing condition mo...

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来源期刊

International Journal of Green Energy 工程技术-能源与燃料

CiteScore

6.60

自引率

9.10%

发文量

112

审稿时长

3.7 months

期刊介绍： International Journal of Green Energy shares multidisciplinary research results in the fields of energy research, energy conversion, energy management, and energy conservation, with a particular interest in advanced, environmentally friendly energy technologies. We publish research that focuses on the forms and utilizations of energy that have no, minimal, or reduced impact on environment, economy and society.