Fiocchi, Filippo;
Ladopoulou, Domna;
Dellaportas, Petros;
(2025)
Probabilistic Multilayer Perceptrons for Wind Farm Condition Monitoring.
Wind Energy
, 28
(4)
, Article e70012. 10.1002/we.70012.
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Abstract
We provide a condition monitoring system for wind farms, based on normal behaviour modelling using a probabilistic multilayer perceptron with transfer learning via fine-tuning. The model predicts the output power of the wind turbine under normal behaviour based on features retrieved from supervisory control and data acquisition (SCADA) systems. Its advantages are that (i) it can be trained with SCADA data of at least a few years, (ii) it can incorporate all SCADA data of all wind turbines in a wind farm as features, (iii) it assumes that the output power follows a normal density with heteroscedastic variance and (iv) it can predict the output of one wind turbine by borrowing strength from the data of all other wind turbines in a farm. Probabilistic guidelines for condition monitoring are given via a cumulative sum (CUSUM) control chart, which is specifically designed based on a real-data classification exercise and, hence, is adapted to the needs of a wind farm. We illustrate the performance of our model in a real SCADA data example which provides evidence that it outperforms other probabilistic prediction models.
| Type: | Article |
|---|---|
| Title: | Probabilistic Multilayer Perceptrons for Wind Farm Condition Monitoring |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/we.70012 |
| Publisher version: | https://onlinelibrary.wiley.com/doi/10.1002/we.700... |
| Language: | English |
| Additional information: | Copyright © 2025 The Author(s). Wind Energy published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, https://creativecommons.org/licenses/by/4.0/, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | CUSUM control chart; fine-tuning; heteroscedasticity; normal behaviour modelling; transfer learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10206306 |
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