Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910350417330176 |
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| author | Backhus, Jana Rao, Aniruddha Rajendra Venkatraman, Chandrasekar Padmanabhan, Abhishek Kumar, A. Vinoth Gupta, Chetan |
| author_facet | Backhus, Jana Rao, Aniruddha Rajendra Venkatraman, Chandrasekar Padmanabhan, Abhishek Kumar, A. Vinoth Gupta, Chetan |
| contents | In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key innovation lies in the ensemble of FNN and LSTM models, capitalizing on their collective learning. This ensemble approach outperforms individual models, ensuring stable and accurate power output predictions. Additionally, machine learning techniques are applied to detect wind turbine performance deterioration, enabling proactive maintenance strategies and health assessment. Crucially, our analysis reveals the uniqueness of each wind turbine, necessitating tailored models for optimal predictions. These insight underscores the importance of providing automatized customization for different turbines to keep human modeling effort low. Importantly, the methodologies developed in this analysis are not limited to wind turbines; they can be extended to predict and optimize performance in various machinery, highlighting the versatility and applicability of our research across diverse industrial contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00975 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance Backhus, Jana Rao, Aniruddha Rajendra Venkatraman, Chandrasekar Padmanabhan, Abhishek Kumar, A. Vinoth Gupta, Chetan Machine Learning Artificial Intelligence Functional Analysis Applications In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key innovation lies in the ensemble of FNN and LSTM models, capitalizing on their collective learning. This ensemble approach outperforms individual models, ensuring stable and accurate power output predictions. Additionally, machine learning techniques are applied to detect wind turbine performance deterioration, enabling proactive maintenance strategies and health assessment. Crucially, our analysis reveals the uniqueness of each wind turbine, necessitating tailored models for optimal predictions. These insight underscores the importance of providing automatized customization for different turbines to keep human modeling effort low. Importantly, the methodologies developed in this analysis are not limited to wind turbines; they can be extended to predict and optimize performance in various machinery, highlighting the versatility and applicability of our research across diverse industrial contexts. |
| title | Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance |
| topic | Machine Learning Artificial Intelligence Functional Analysis Applications |
| url | https://arxiv.org/abs/2403.00975 |