Summary of Probabilistic Multi-layer Perceptrons For Wind Farm Condition Monitoring, by Filippo Fiocchi et al.
Probabilistic Multi-Layer Perceptrons for Wind Farm Condition Monitoring
by Filippo Fiocchi, Domna Ladopoulou, Petros Dellaportas
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel condition monitoring system for wind farms is proposed, utilizing normal behavior modeling with a probabilistic multi-layer perceptron and transfer learning via fine-tuning. The model predicts wind turbine output power based on features retrieved from supervisory control and data acquisition (SCADA) systems. Its advantages include the ability to train using SCADA data from at least several years, incorporating all SCADA data from multiple turbines as features, assuming normal density with heteroscedastic variance, and predicting individual turbine output by borrowing strength from other farm turbines. Probabilistic guidelines for condition monitoring are provided via a cumulative sum (CUSUM) control chart, specifically designed based on real-data classification exercise. The model outperforms other probabilistic prediction models in a real SCADA data example. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new system helps keep wind farms running smoothly by using math to predict what the turbines will do next. It looks at past performance and adjusts for unusual patterns. This makes it better than earlier systems because it can learn from many different turbines and use that information to make predictions. The system is good at catching problems before they cause big issues, which means wind farms can be more reliable and less expensive to maintain. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Transfer learning
