Abstract

This paper proposes a failure prediction system for wind turbines using the Normal Behavior Model (NBM) approach. By using available SCADA data, the NBMs are trained to make predictions that reflect what would be a turbine’s normal operating condition. They are able to identify when a given operating condition is abnormal, which points towards probable component degradation. Alerts are raised based on the daily-averaged prediction error to help the O&M team in identifying turbines that need maintenance. The NBMs are comprised of numerous linear models with different inputs and training sets, according to an ensemble approach that aims to avoid overfitting and to reduce the amount of false-positive predictions.

Description and insights on various development steps are presented, such as data treatment, model selection, error calculation and alerts generations. Two test cases are shown using operational data from existing wind turbines, highlighting the system’s ability to generate alerts weeks before a severe fault occurs.

Details

Title
Wind Turbine Failure Prediction Using SCADA Data
Author
Lima, L A M 1 ; Blatt, A 2 ; Fujise, J 2 

 Voltalia - Rua do Passeio 78, 14° andar, Centro, 20021-290, Rio de Janeiro, RJ, Brazil; Electrical Engineering Program, COPPE-UFRJ, Cidade Universitaria, Centro de Tecnologia, bloco H, 21941-972, Rio de Janeiro, RJ, Brazil 
 Voltalia - Rua do Passeio 78, 14° andar, Centro, 20021-290, Rio de Janeiro, RJ, Brazil 
Publication year
2020
Publication date
Sep 2020
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2620837346
Copyright
© 2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.