An ABC-SVM based Fault Prognosis of Wind Turbines using SCADA Data

Articles in Press

Document Type : Article

Authors

1 Assistant Professor, Kamaraj College of Engineering and Technology

2 1/3030 Muthal Nagar Pandian Nagar

3 Kamaraj College of Engineering and Technology

Abstract

Fault prediction and diagnosis play an essential role in safe and reliable operation of wind turbines (WT). An efficient fault prognosis method will help in the earlier identification of WT faults and failures, thereby reducing WT maintenance costs and improving their operating time. WT’s are often controlled by Supervisory Control and Data Acquisition (SCADA), which, apart from controlling, provides very rich data pertaining to working parameters of WT's. SCADA data, along with suitable algorithms, could be used for fault prediction and diagnosis of WT's. Most of WT prognosis systems make use of Support Vector Machines in conjunction with SCADA data to predetermine the faults that might occur in the turbines in near future. In these models, proper selection of SVM parameters is essential for precise fault classification. In this work, an optimised methodology using Artificial Bee Colony Optimisation (ABC) is proposed to find an optimal penalty factor and kernel function parameter that guarantees better classification accuracy for the SVM model. Based on the real time SCADA data availed from a wind farm, it is observed that the proposed ABC-SVM fault diagnosis model has a quick convergence rate and good accuracy compared to the other GA-SVM, PSO-SVM, and ACO-SVM based models.

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Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 19 May 2024

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History

  • Receive Date: 11 July 2023
  • Revise Date: 27 October 2023
  • Accept Date: 18 May 2024

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