@inproceedings{8ea03bdabece4aa9914cdb4d1a80ba24,
title = "A Data-Driven Fault Diagnosis Approach for Anemometers in Wind Farm",
abstract = "Cup anemometers are widely used instruments for wind turbines to measure wind speed in wind farm. Aimed to reduce the adverse impact on wind energy resource estimation, this paper proposes a data-driven fault diagnosis approach for assessing the anemometer health status. Auto-associative netural network (AANN) is developed to reconstruct the anemometer measurement data after data pre-processing, and residual analysis is performed between the anemometer measurement data and the AANN reconstruction data. In addition, the quantitative indicators that can reflect the health status of the anemometer gained from residuals are obtained through the K-Means clustering algorithm, based on which the faulty anemometers in the wind farm can be identified. The approach can provide guidance for the production and operation of the wind farm.",
keywords = "K-Means clustering, anemometer, auto-associative neural network, fault diagnosis, wind farm",
author = "Jiusi Zhang and Kuan Li and Hao Luo and Shen Yin",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 ; Conference date: 19-10-2020 Through 21-10-2020",
year = "2020",
month = oct,
day = "18",
doi = "10.1109/IECON43393.2020.9254833",
language = "英语",
series = "IECON Proceedings (Industrial Electronics Conference)",
publisher = "IEEE Computer Society",
pages = "405--410",
booktitle = "Proceedings - IECON 2020",
address = "美国",
}