Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation

Ainhoa Pujana (), Miguel Esteras, Eugenio Perea, Erik Maqueda and Philippe Calvez
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Ainhoa Pujana: TECNALIA, Basque Research and Technology Alliance (BRTA), Edificio 700, 48160 Derio, Spain
Miguel Esteras: TECNALIA, Basque Research and Technology Alliance (BRTA), Edificio 700, 48160 Derio, Spain
Eugenio Perea: TECNALIA, Basque Research and Technology Alliance (BRTA), Edificio 700, 48160 Derio, Spain
Erik Maqueda: TECNALIA, Basque Research and Technology Alliance (BRTA), Edificio 700, 48160 Derio, Spain
Philippe Calvez: ENGIE LAB CRIGEN, 1 Place Samuel De Champlain, 92400 Courbevoie, France

Energies, 2023, vol. 16, issue 2, 1-20

Abstract: Computer modelling and digitalization are integral to the wind energy sector since they provide tools with which to improve the design and performance of wind turbines, and thus reduce both capital and operational costs. The massive sensor rollout and increase in big data processing capacity over the last decade has made data collection and analysis more efficient, allowing for the development and use of digital twins. This paper presents a methodology for developing a hybrid-model-based digital twin (DT) of a power conversion system of wind turbines. This DT allows knowledge to be acquired from real operation data while preserving physical design relationships, can generate synthetic data from events that never happened, and helps in the detection and classification of different failure conditions. Starting from an initial physics-based model of a wind turbine drivetrain, which is trained with real data, the proposed methodology has two major innovative outcomes. The first innovation aspect is the application of generative stochastic models coupled with a hybrid-model-based digital twin (DT) for the creation of synthetic failure data based on real anomalies observed in SCADA data. The second innovation aspect is the classification of failures based on machine learning techniques, that allows anomaly conditions to be identified in the operation of the wind turbine. Firstly, technique and methodology were contrasted and validated with operation data of a real wind farm owned by Engie, including labelled failure conditions. Although the selected use case technology is based on a double-fed induction generator (DFIG) and its corresponding partial-scale power converter, the methodology could be applied to other wind conversion technologies.

Keywords: wind turbine; digital twin; hybrid model; failure diagnosis; synthetic data generation; predictive maintenance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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