A reliable detection and characterisation of the different operating modes of a wind turbine is essential for the correct understanding of its behaviour and production performance. Wind turbines are usually installed in fleets, which offers richer datasets to exploit. However, blindly applying machine learning approaches to such datasets may mask turbine-specific features. Moreover, data originating from wind turbines often contain multiple heterogenous subsets of parameters, which not always can be considered simultaneously during analysis. We propose a method for advanced exploration of wind fleet data by uniformly converting the time series sensor measurements across the fleet into a discrete code of operating mode labels by use of the data exploration framework proposed by Dhont et al. (2020a). Subsequently, we illustrate the potential of the method to derive relevant insights about the turbine performance on a real-world dataset of a fleet of 4 turbines.
Dhont, M , Verbeke, R, Droutsas, C, Boeva, V, Verbeke, M, Murgia, A & Tsiporkova, E 2021, Advanced exploration of wind fleet data through operating mode labelling . in Proceedings 5th RESRB 2020: Renewable Energy Sources – Research and Business. vol. 5, Wojciech Budzianowski Consulting Services, pp. 1-4, Renewable Energy Sources – Research and Business 2020, Brussels, Belgium, 7/09/20 .
Dhont, M. , Verbeke, R., Droutsas, C., Boeva, V., Verbeke, M., Murgia, A., & Tsiporkova, E. (2021). Advanced exploration of wind fleet data through operating mode labelling . In Proceedings 5th RESRB 2020: Renewable Energy Sources – Research and Business (Vol. 5, pp. 1-4). Wojciech Budzianowski Consulting Services.
@inproceedings{472eb00d6b5e443b8eaf3a768a09c9e3,
title = " Advanced exploration of wind fleet data through operating mode labelling " ,
abstract = " A reliable detection and characterisation of the different operating modes of a wind turbine is essential for the correct understanding of its behaviour and production performance. Wind turbines are usually installed in fleets, which offers richer datasets to exploit. However, blindly applying machine learning approaches to such datasets may mask turbine-specific features. Moreover, data originating from wind turbines often contain multiple heterogenous subsets of parameters, which not always can be considered simultaneously during analysis. We propose a method for advanced exploration of wind fleet data by uniformly converting the time series sensor measurements across the fleet into a discrete code of operating mode labels by use of the data exploration framework proposed by Dhont et al. (2020a). Subsequently, we illustrate the potential of the method to derive relevant insights about the turbine performance on a real-world dataset of a fleet of 4 turbines. " ,
keywords = " Wind Turbine, Operating Mode Labelling, Multi-source Data, Performance Monitoring " ,
author = " Michiel Dhont and Robbert Verbeke and Christos Droutsas and Veselka Boeva and Mathias Verbeke and Alessandro Murgia and Elena Tsiporkova " ,
year = " 2021 " ,
month = dec,
day = " 8 " ,
language = " English " ,
volume = " 5 " ,
pages = " 14 " ,
booktitle = " Proceedings 5th RESRB 2020 " ,
publisher = " Wojciech Budzianowski Consulting Services " ,
note = " Renewable Energy Sources – Research and Business 2020, RESRB 2020 Conference date: 07-09-2020 Through 08-09-2020 " ,
url = " http://resrb.budzianowski.eu/ " ,
}