In this paper, we introduce a data-driven framework which combines a feature derivation to be the backbone of a behaviour model developed for fleet of wind turbines. The feature derivation component involves the use of a multivariate feature selection algorithm based on a novel Combined Power Predictive Score (CPPS), where the information content of combinations of variables is considered for the prediction of one or more key parameters. A community detection algorithm, based on Complex Network Analysis, is then used to unveil groups of wind turbine (within a fleet) featuring similar behaviour. The algorithm was tested on about 50 wind turbines, including diverse OEMs. The proposed algorithm employs the XGBoost regressor considering lagged time series to compute the Combined Predictive Power Score, and the results demonstrate the flexibility of the multi-input multi-output formulation, outperforming the standard monovariate formulation. The framework is used on active power target and it demonstrates to be a viable option in normal behaviour modelling derivation as it permits to isolate communities of wind turbines based on their actual operational history. This circumstance promised to be the forerunner of asset-specific O&M strategies.
Skip Nav Destination
A Systematic Study on the Use of Machine Learnt Feature Derivation in Horizontal Axis Wind Turbine Fleets
Alessandro Corsini;
Alessandro Corsini
Sapienza University
Search for other works by this author on:
Paper presented at the OMC Med Energy Conference and Exhibition, Ravenna, Italy, October 2023.
Paper Number:
OMC-2023-428
Published:
October 24 2023
Citation
Barnabei, Valerio, Morvillo, Emanuele, Corsini, Alessandro, and Fabrizio Bonacina. "A Systematic Study on the Use of Machine Learnt Feature Derivation in Horizontal Axis Wind Turbine Fleets." Paper presented at the OMC Med Energy Conference and Exhibition, Ravenna, Italy, October 2023.
Download citation file:
Sign in
Don't already have an account? Register
Personal Account
You could not be signed in. Please check your username and password and try again.
Captcha Validation Error. Please try again.
Pay-Per-View Access
$10.00
Advertisement
13
Views
Advertisement
Suggested Reading
Advertisement