Estimation of Wind Turbine Performance Degradation with Deep Neural Networks

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Published Jun 29, 2022
Manuel S Mathew Surya Teja Kandukuri Christian W Omlin

Abstract

In this paper, we estimate the age-related performance degradation of a wind turbine working under Norwegian environment, based on a deep neural network model. Ten years of high-resolution operational data from a 2 MW wind turbine were used for the analysis. Operational data of the turbine, between cut-in and rated wind velocities, were considered, which were pre-processed to eliminate outliers and noises. Based on the SHapley Additive exPlanations of a preliminary performance model, a benchmark performance model for the turbine was developed with deep neural networks. An efficiency index is proposed to gauge the agerelated performance degradation of the turbine, which compares measured performances of the turbine over the years with corresponding bench marked performance. On an average, the efficiency index of the turbine is found to decline by 0.64 percent annually, which is comparable with the degradation patterns reported under similar studies from the UK and the US.

How to Cite

Mathew, M. S., Kandukuri, S. T. ., & Omlin, C. W. (2022). Estimation of Wind Turbine Performance Degradation with Deep Neural Networks. PHM Society European Conference, 7(1), 351–359. https://doi.org/10.36001/phme.2022.v7i1.3328
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Keywords

Deep Neural Networks, Degradation, Wind Turbine

Section
Technical Papers