The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network
Abstract
Abstract Continuous monitoring of wind turbine health conditions using anomaly detection methods can improve the reliability and reduce maintenance costs during operation of wind turbine. Anomaly detection aims at identifying the root causes leading to unexpected changes of product performance. Most existing methods make less use of temporal order of the data and are poor at extracting features from these data. To address these problems, a method based on long short-term memory (LSTM) and auto-encoder (AE) neural network is introduced to assess sequential condition monitoring data of the wind turbine. First, a performance assessment model is constructed using LSTM neural units and AE networks to calculate the performance indices for evaluation of the degree of anomalies in wind turbine performance. Then, an adaptive threshold estimation method based on support vector regression model is developed to identify the abnormal data instances. The mutual information theory is subsequently explored to analyze the relationships between various monitoring parameters and performance abnormal instances to identify critical condition monitoring parameters. The effectiveness of the proposed method has been verified by a case study using real-world wind turbine condition monitoring (CM) data.
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Cited by
Li, G., & Jung, J. J. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.10.008 · Cited by 361
Afridi, Y. S., Ahmad, K., & Hassan, L. (2022) · International Journal of Energy Research · doi:10.1002/er.7100 · Cited by 140
Metadata
- Authors
- Chen, H., Liu, H., Chu, X., Liu, Q., & Xue, D.
- Venue
- Renewable Energy
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 172
- Pages
- 829-840
- ISSN
- 0960-1481 (ISSN-L)
- OpenAlex
- W3138430000
- Cited by
- 253
- References
- 33
- FWCI
- 16.52
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, H., Liu, H., Chu, X., Liu, Q., & Xue, D. (2021). Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network. Renewable Energy. https://doi.org/10.1016/j.renene.2021.03.078
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