Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines
Monitoring wind turbine performance is vital for ensuring wind turbines’ safe, efficient, and cost-effective operation over time. Using principal component analysis (PCA), k-means clustering for labeling, and an ensemble classifier for finding outliers, this study suggests a new way to fi...
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| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
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| Online Access: | https://ieeexplore.ieee.org/document/10621021/ |
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| author | Prince Waqas Khan Yung-Cheol Byun |
| author_facet | Prince Waqas Khan Yung-Cheol Byun |
| author_sort | Prince Waqas Khan |
| collection | DOAJ |
| description | Monitoring wind turbine performance is vital for ensuring wind turbines’ safe, efficient, and cost-effective operation over time. Using principal component analysis (PCA), k-means clustering for labeling, and an ensemble classifier for finding outliers, this study suggests a new way to find anomalies in wind turbines. The primary objective is to improve the precision of anomaly detection in wind turbines by leveraging machine-learning techniques. The proposed methodology utilizes the output of the PCA-Kmeans model to label supervisory control and data acquisition (SCADA) data. Furthermore, a stacking ensemble classifier is employed to refine the model’s precision. Our proposed model achieved a classification accuracy of 99%, which is a significant improvement compared to existing approaches. The significance of this study lies in its potential to enable more efficient wind turbine operation by identifying and resolving anomalies that may reduce their performance. This can ultimately contribute to achieving a sustainable and renewable energy future. |
| format | Article |
| id | doaj-art-c098636e10d2487d9af9ab0e75bbaf7f |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-c098636e10d2487d9af9ab0e75bbaf7f2025-01-21T00:03:19ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102024-01-011134936110.1109/OAJPE.2024.343741410621021Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind TurbinesPrince Waqas Khan0https://orcid.org/0000-0002-2561-4389Yung-Cheol Byun1https://orcid.org/0000-0003-1107-9941Department of Industrial and Management Systems Engineering, West Virginia University, Morgantown, WV, USADepartment of Computer Engineering, Jeju National University, Jeju-si, South KoreaMonitoring wind turbine performance is vital for ensuring wind turbines’ safe, efficient, and cost-effective operation over time. Using principal component analysis (PCA), k-means clustering for labeling, and an ensemble classifier for finding outliers, this study suggests a new way to find anomalies in wind turbines. The primary objective is to improve the precision of anomaly detection in wind turbines by leveraging machine-learning techniques. The proposed methodology utilizes the output of the PCA-Kmeans model to label supervisory control and data acquisition (SCADA) data. Furthermore, a stacking ensemble classifier is employed to refine the model’s precision. Our proposed model achieved a classification accuracy of 99%, which is a significant improvement compared to existing approaches. The significance of this study lies in its potential to enable more efficient wind turbine operation by identifying and resolving anomalies that may reduce their performance. This can ultimately contribute to achieving a sustainable and renewable energy future.https://ieeexplore.ieee.org/document/10621021/Wind turbineanomaly detectionprincipal component analysisk-means clusteringlabelingensemble classifier |
| spellingShingle | Prince Waqas Khan Yung-Cheol Byun Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines IEEE Open Access Journal of Power and Energy Wind turbine anomaly detection principal component analysis k-means clustering labeling ensemble classifier |
| title | Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines |
| title_full | Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines |
| title_fullStr | Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines |
| title_full_unstemmed | Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines |
| title_short | Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines |
| title_sort | detecting anomaly classification using pca kmeans and ensembled classifier for wind turbines |
| topic | Wind turbine anomaly detection principal component analysis k-means clustering labeling ensemble classifier |
| url | https://ieeexplore.ieee.org/document/10621021/ |
| work_keys_str_mv | AT princewaqaskhan detectinganomalyclassificationusingpcakmeansandensembledclassifierforwindturbines AT yungcheolbyun detectinganomalyclassificationusingpcakmeansandensembledclassifierforwindturbines |