دورية أكاديمية
Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis
| العنوان: | Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis |
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| المؤلفون: | K. Ramakrishna Kini, Fouzi Harrou, Muddu Madakyaru, Ying Sun |
| المصدر: | Energies, Vol 16, Iss 15, p 5793 (2023) |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Technology |
| مصطلحات موضوعية: | wind turbines, SCADA data, sensor faults, semi-supervised monitoring, data-driven methods, dynamic PCA, Technology |
| الوصف: | Efficient detection of sensor faults in wind turbines is essential to ensure the reliable operation and performance of these renewable energy systems. This paper presents a novel semi-supervised data-based monitoring technique for fault detection in wind turbines using SCADA (supervisory control and data acquisition) data. Unlike supervised methods, the proposed approach does not require labeled data, making it cost-effective and practical for wind turbine monitoring. The technique builds upon the Independent Component Analysis (ICA) approach, effectively capturing non-Gaussian features. Specifically, a dynamic ICA (DICA) model is employed to account for the temporal dynamics and dependencies in the observed signals affected by sensor faults. The fault detection process integrates fault indicators based on I2d, I2e, and squared prediction error (SPE), enabling the identification of different types of sensor faults. The fault indicators are combined with a Double Exponential Weighted Moving Average (DEWMA) chart, known for its superior performance in detecting faults with small magnitudes. Additionally, the approach incorporates kernel density estimation to establish nonparametric thresholds, increasing flexibility and adaptability to different data types. This study considers various types of sensor faults, including bias sensor faults, precision degradation faults, and freezing sensor faults, for evaluation. The results demonstrate that the proposed approach outperforms PCA and traditional ICA-based methods. It achieves a high detection rate, accurately identifying faults while reducing false alarms. It could be a promising technique for proactive maintenance, optimizing the performance and reliability of wind turbine systems. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 16155793 1996-1073 |
| Relation: | https://www.mdpi.com/1996-1073/16/15/5793; https://doaj.org/toc/1996-1073 |
| DOI: | 10.3390/en16155793 |
| URL الوصول: | https://doaj.org/article/e04d2b5d25724d9e82aca508385f17fa |
| رقم الأكسشن: | edsdoj.04d2b5d25724d9e82aca508385f17fa |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 16155793 19961073 |
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| DOI: | 10.3390/en16155793 |