# Multi-target normal behaviour models for wind farm condition monitoring

arXiv: https://arxiv.org/abs/2012.03074 · HTML: https://mlsanity.com/paper/2012.03074/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Vibration fault detection in wind turbines based on normal behaviour models without feature engineering](https://mlsanity.com/paper/2206.12452/) (2022)
- [Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning](https://mlsanity.com/paper/2212.03529/) (2022)
- [Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learning](https://mlsanity.com/paper/2504.17709/) (2025)
- [Anomaly Detection in Power Markets and Systems](https://mlsanity.com/paper/2212.02182/) (2022)
- [Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss](https://mlsanity.com/paper/2506.13012/) (2025)
- [Early wind turbine alarm prediction based on machine learning: AlarmForecasting](https://mlsanity.com/paper/2510.06831/) (2025)
- [Generative multi-domain transfer learning for fault detection in data-scarce wind turbines](https://mlsanity.com/paper/2608.30323/) (2026)

## Abstract (arXiv)

The trend towards larger wind turbines and remote locations of wind farms fuels the demand for automated condition monitoring strategies that can reduce the operating cost and avoid unplanned downtime. Normal behaviour modelling has been introduced to detect anomalous deviations from normal operation based on the turbine's SCADA data. A growing number of machine learning models of the normal behaviour of turbine subsystems are being developed by wind farm managers to this end. However, these models need to be kept track of, be maintained and require frequent updates. This research explores multi-target models as a new approach to capturing a wind turbine's normal behaviour. We present an overview of multi-target regression methods, motivate their application and benefits in wind turbine condition monitoring, and assess their performance in a wind farm case study. We find that multi-target models are advantageous in comparison to single-target modelling in that they can reduce the cost and effort of practical condition monitoring without compromising on the accuracy. We also outline some areas of future research.