Open Access

ARTICLE

SCADA Data-Based Support Vector Machine for False Alarm Identification for Wind Turbine Management

Ana María Peco Chacón, Isaac Segovia Ramírez, Fausto Pedro García Márquez^*

Ingenium Research Group, Universidad Castilla-La Mancha, Ciudad Real, 13071, Spain

* Corresponding Author: Fausto Pedro García Márquez. Email:

(This article belongs to the Special Issue: Data Analytics for Critical Infrastructures)

Intelligent Automation & Soft Computing 2023, 37(3), 2595-2608. https://doi.org/10.32604/iasc.2023.037277

Received 28 October 2022; Accepted 24 February 2023; Issue published 11 September 2023

Abstract

Maintenance operations have a critical influence on power generation by wind turbines (WT). Advanced algorithms must analyze large volume of data from condition monitoring systems (CMS) to determine the actual working conditions and avoid false alarms. This paper proposes different support vector machine (SVM) algorithms for the prediction and detection of false alarms. K-Fold cross-validation (CV) is applied to evaluate the classification reliability of these algorithms. Supervisory Control and Data Acquisition (SCADA) data from an operating WT are applied to test the proposed approach. The results from the quadratic SVM showed an accuracy rate of 98.6%. Misclassifications from the confusion matrix, alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm. The classifier reduces the number of false alarms called misclassifications by 25%. These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.

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Keywords

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