# Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature

arXiv: https://arxiv.org/abs/2209.15579 · HTML: https://mlsanity.com/paper/2209.15579/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [GPflow: A Gaussian process library using TensorFlow](https://mlsanity.com/paper/1610.08733/) (2016)
- [A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges](https://mlsanity.com/paper/2006.09319/) (2020)
- [Chained Gaussian Processes](https://mlsanity.com/paper/1604.05263/) (2016)
- [Real-time predictive maintenance for wind turbines using Big Data frameworks](https://mlsanity.com/paper/1709.07250/) (2017)
- [A parametric model for wind turbine power curves incorporating environmental conditions](https://mlsanity.com/paper/1909.13780/) (2019)

## Built on by (most cited first, 4 of 4)

- [A spectrum of physics-informed Gaussian processes for regression in engineering](https://mlsanity.com/paper/2309.10656/) (2023)
- [Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification](https://mlsanity.com/paper/2307.14675/) (2023)
- [A new perspective on Bayesian Operational Modal Analysis](https://mlsanity.com/paper/2408.08664/) (2024)
- [Inter-turbine Modelling of Wind-Farm Power using Multi-task Learning](https://mlsanity.com/paper/2502.14527/) (2025)

## Abstract (arXiv)

A wind turbines' power curve is easily accessible damage sensitive data, and as such is a key part of structural health monitoring in wind turbines. Power curve models can be constructed in a number of ways, but the authors argue that probabilistic methods carry inherent benefits in this use case, such as uncertainty quantification and allowing uncertainty propagation analysis. Many probabilistic power curve models have a key limitation in that they are not physically meaningful - they return mean and uncertainty predictions outside of what is physically possible (the maximum and minimum power outputs of the wind turbine). This paper investigates the use of two bounded Gaussian Processes in order to produce physically meaningful probabilistic power curve models. The first model investigated was a warped heteroscedastic Gaussian process, and was found to be ineffective due to specific shortcomings of the Gaussian Process in relation to the warping function. The second model - an approximated Gaussian Process with a Beta likelihood was highly successful and demonstrated that a working bounded probabilistic model results in better predictive uncertainty than a corresponding unbounded one without meaningful loss in predictive accuracy. Such a bounded model thus offers increased accuracy for performance monitoring and increased operator confidence in the model due to guaranteed physical plausibility.