TY  - EJOU
AU  - Chacón, Ana María Peco 
AU  - Ramírez, Isaac Segovia 
AU  - Márquez, Fausto Pedro García 

TI  - SCADA Data-Based Support Vector Machine for False Alarm Identification for Wind Turbine Management
T2  - Intelligent Automation \& Soft Computing

PY  - 2023
VL  - 37
IS  - 3
SN  - 2326-005X

AB  - 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.
KW  - Machine learning classification; support vector machine; false alarm; wind turbine; cross-validation

DO  - 10.32604/iasc.2023.037277