Operational Wind Turbine Blade Damage Evaluation Based on 10-min SCADA and 1 Hz Data

Antoine Chrétien (), Antoine Tahan, Philippe Cambron and Adaiton Oliveira-Filho
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Antoine Chrétien: Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
Antoine Tahan: Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
Philippe Cambron: Department of Advanced Analytics Research, Power Factors, Brossard, QC J4Z 1A7, Canada
Adaiton Oliveira-Filho: Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada

Energies, 2023, vol. 16, issue 7, 1-18

Abstract: This work aims to propose a method enabling the evaluation of wind turbine blade damage and fatigue related to a 1 Hz wind speed signal applied to a large period and based on standard 10-min SCADA data. Previous studies emphasize the need for sampling with a 1 Hz frequency when carrying out blade damage computation. However, such methods cannot be applied to evaluate the damage for a long period of time due to the complexity of computation and data availability. Moreover, 1 Hz SCADA data are not commonly used in the wind farm industry because they require a large data storage capacity. Applying such an approach, which is based on a 1 Hz wind speed signal, to current wind farms is not a trivial pursuit. The present work investigates the possibility of overcoming the preceding issues by estimating the equivalent 1 Hz wind speed damage over a 10-min period characterized by SCADA data in terms of measured mean wind speed and turbulence intensity. Then, a discussion is carried out regarding a method to estimate the uncertainty of the simulation, in a bid to come up with a tool facilitating decision-making by the operator. A statistical analysis of the damage assessed for different wind turbines is thus proposed to determine which one has sustained the most damage. Finally, the probability of reaching a critical damage level over time is then proposed, allowing the operator to optimize the operating and maintenance schedule.

Keywords: predictive maintenance; wind turbine blade; rainflow counting; damage estimation; composite materials (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
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