Issue |
E3S Web Conf.
Volume 433, 2023
2023 The 6th International Conference on Renewable Energy and Environment Engineering (REEE 2023)
|
|
---|---|---|
Article Number | 01008 | |
Number of page(s) | 11 | |
Section | Environmental Chemical Engineering and Environmental Impact Assessment of the Construction Industry | |
DOI | https://doi.org/10.1051/e3sconf/202343301008 | |
Published online | 09 October 2023 |
Intelligent digital twin – machine learning system for real-time wind turbine wind speed and power generation forecasting
1 University of Hull, School of Computer Science, HU6 7RX, Hull, United Kingdom
2 Aura Centre for Doctoral Training in Offshore Wind Energy and the Environment, University of Hull, HU6 7RX, Hull, United Kingdom
* Corresponding author: e.tuton-2021@hull.ac.uk
Wind power is a key pillar in efforts to decarbonise energy production. However, variability in wind speed and resultant wind turbine power generation poses a challenge for power grid integration. Digital Twin (DT) technology provides intelligent service systems, combining real-time monitoring, predictive capabilities and communication technologies. Current DT research for wind turbine power generation has focused on providing wind speed and power generation predictions reliant on Supervisory Control and Data Acquisition (SCADA) sensors, with predictions often limited to the timeframe of datasets. This research looks to expand on this, utilising a novel framework for an intelligent DT system powered by k-Nearest Neighbour (kNN) regression models to upscale live wind speed forecasts to higher wind turbine hub-height and then forecast power generation. As there is no live link to a wind turbine, the framework is referred to as a “Simulated Digital Twin” (SimTwin). 2019-2020 SCADA and wind speed data are used to evaluate this, demonstrating that the method provides suitable predictions. Furthermore, full deployment of the SimTwin framework is demonstrated using live wind speed forecasts. This may prove useful for operators by reducing reliance on SCADA systems and provides a research and development tool where live data is limited.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.