Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2vec

Author:

Wei Lu1,Wang Liliang2,Liu Feng1,Qian Zheng2

Affiliation:

1. School of Electronics and Information Engineering, Beihang University, Beijing 100191, China

2. School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China

Abstract

The alarm data contain abundant fault information related to almost all components of the wind turbine. Reasonable analysis and utilization of alarm data can assist wind farm maintenance personnel in quickly identifying the types of turbine faults, reducing operation and maintenance costs. This paper proposes a clustering analysis method that groups similar alarm sequences with the same fault type. Firstly, the alarm data are preprocessed, where alarm sequences are segmented, and redundant alarms are removed. Then, a domain knowledge-fused Word2vec (DK-Wrod2vec) method is introduced to transform non-numeric alarm codes into numeric vector representations. Finally, new distance metrics are incorporated into the K-means clustering algorithm to improve clustering performance. The performance of the proposed clustering method is assessed by applying it to labeled alarm sequences. The results demonstrate that the clustering performance is the best when using DK-Word2vec and the word rotator’s distance compared with other methods. Additionally, with the optimal parameter combination, the fault types of unlabeled alarm sequences are also analyzed.

Funder

National Natural Science Foundation of China

Program for Changjiang Scholars and Innovative Research Team in University

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference32 articles.

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4. Acoustic emission based damage source localization for structural digital twin of wind turbine blades;Zhao;Ocean Eng.,2022

5. Nonlinear system identification for model-based condition monitoring of wind turbines;Cross;Renew. Energy,2014

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