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Turkish Journal of Electrical Engineering and Computer Sciences

Author ORCID Identifier

VEILRAJ REVATHI: 0000-0003-3349-082X

SOLAIMALAI JEYADEVI: 0000-0001-6591-8187

MADASAMY SUDALAIMANI: 0000-0001-6373-8879

Abstract

The complex electromechanical structure of wind turbines, along with harsh operating conditions, poses significant challenges for precise and robust fault diagnosis. To address this challenge, an ensemble multifault diagnostic framework based on an adaptive chaotic artificial bee colony (C-ABC)-optimized support vector machine (SVM) and gradient boosting machine (GBM) is proposed. In the proposed framework, data redundancy and overfitting are reduced through a two-stage hybrid filter-transformer-based feature reduction approach using ReliefF, followed by Principal Component Analysis. The chaos function of the proposed C-ABC maintains an adaptive balance between the exploration and exploitation phases, thereby preventing premature convergence, which is a common problem in the traditional ABC, and ensuring optimal hyperparameters for the SVM and GBM classifiers. These optimized models were further stacked into an ensemble framework that leverages the individual strengths of the SVM and GBM classifiers, providing a multifault diagnostic model with a complementary decision boundary and enhanced multifault detection capability. Compared with conventional fault diagnosis methods, the proposed ensemble framework achieved superior classification accuracy (98.5\%) and classification stability during experimental validation using real-world SCADA data. Pseudo cross-turbine validation using real-time SCADA data was conducted to evaluate and verify the generalization ability of the proposed ensemble framework.

DOI

10.55730/1300-0632.4186

Keywords

Fault diagnosis, chaotic artificial bee colony algorithm, parameter optimization, gradient boosting machine, support vector machine, wind turbines

First Page

472

Last Page

488

Publisher

The Scientific and Technological Research Council of Türkiye (TÜBİTAK)

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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