Estimating the Remaining Power Generation of Wind Turbines—An Exploratory Study for Main Bearing Failures

Benedikt Wiese, Niels L. Pedersen, Esmaeil S. Nadimi and Jürgen Herp
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Benedikt Wiese: The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense M, Denmark
Niels L. Pedersen: Diagnostics, Siemens Gamesa Renewable Energy, 7330 Brande, Denmark
Esmaeil S. Nadimi: The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense M, Denmark
Jürgen Herp: The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense M, Denmark

Energies, 2020, vol. 13, issue 13, 1-11

Abstract: Condition monitoring for wind turbines is tailored to predict failure and aid in making better operation and maintenance (O&M) decisions. Typically the condition monitoring approaches are concerned with predicting the remaining useful lifetime (RUL) of assets or a component. As the time-based measures can be rendered absolute when changing the operational set-point of a wind turbine, we propose an alternative in a power-based condition monitoring framework for wind turbines, i.e., the remaining power generation (RPG) before a main bearing failure. The proposed model utilizes historic wind turbine data, from both run-to-failure and non run-to-failure turbines. Comprised of a recurrent neural network with gated recurrent units, the model is constructed around a censored and uncensored data-based cost function. We infer a Weibull distribution over the RPG, which gives an operator a measure of how certain any given prediction is. As part of the model evaluation, we present the hyper-parameter selection, as well as modeling error in detail, including an analysis of the driving features. During the application on wind turbine main bearing failures, we achieve prediction in the magnitude of 1 to 2 GWh before the failure. When converting to RUL this corresponds to predicting the failure, on average, 81 days beforehand, which is comparable to the state-of-the-art’s 94 days predictive horizon in a similar feature space.

Keywords: wind turbines; remaining power generation; main bearing; neural network (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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