Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier
Wind turbines are widely used worldwide to generate clean, renewable energy. The biggest issue with a wind turbine is reducing failures and downtime, which lowers costs associated with operations and maintenance. Wind turbines’ consistency and timely maintenance can enhance their performance and dep...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/18/6955 |
| _version_ | 1827656777186934784 |
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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 | Wind turbines are widely used worldwide to generate clean, renewable energy. The biggest issue with a wind turbine is reducing failures and downtime, which lowers costs associated with operations and maintenance. Wind turbines’ consistency and timely maintenance can enhance their performance and dependability. Still, the traditional routine configuration makes detecting faults of wind turbines difficult. Supervisory control and data acquisition (SCADA) produces reliable and affordable quality data for the health condition of wind turbine operations. For wind power to be sufficiently reliable, it is crucial to retrieve useful information from SCADA successfully. This article proposes a new AdaBoost, K-nearest neighbors, and logistic regression-based stacking ensemble (AKL-SE) classifier to classify the faults of the wind turbine condition monitoring system. A stacking ensemble classifier integrates different classification models to enhance the model’s accuracy. We have used three classifiers, AdaBoost, K-nearest neighbors, and logistic regression, as base models to make output. The output of these three classifiers is used as input in the logistic regression classifier’s meta-model. To improve the data validity, SCADA data are first preprocessed by cleaning and removing any abnormal data. Next, the Pearson correlation coefficient was used to choose the input variables. The Stacking Ensemble classifier was trained using these parameters. The analysis demonstrates that the suggested method successfully identifies faults in wind turbines when applied to local 3 MW wind turbines. The proposed approach shows the potential for effective wind energy use, which could encourage the use of clean energy. |
| first_indexed | 2024-03-09T22:34:04Z |
| format | Article |
| id | doaj.art-a7465dffbec94432bef72499dcb3cca4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:34:04Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a7465dffbec94432bef72499dcb3cca42023-11-23T18:52:00ZengMDPI AGSensors1424-82202022-09-012218695510.3390/s22186955Multi-Fault Detection and Classification of Wind Turbines Using Stacking ClassifierPrince Waqas Khan0Yung-Cheol Byun1Department of Computer Engineering, Jeju National University, Jeju-si 63243, KoreaDepartment of Computer Engineering, Jeju National University, Jeju-si 63243, KoreaWind turbines are widely used worldwide to generate clean, renewable energy. The biggest issue with a wind turbine is reducing failures and downtime, which lowers costs associated with operations and maintenance. Wind turbines’ consistency and timely maintenance can enhance their performance and dependability. Still, the traditional routine configuration makes detecting faults of wind turbines difficult. Supervisory control and data acquisition (SCADA) produces reliable and affordable quality data for the health condition of wind turbine operations. For wind power to be sufficiently reliable, it is crucial to retrieve useful information from SCADA successfully. This article proposes a new AdaBoost, K-nearest neighbors, and logistic regression-based stacking ensemble (AKL-SE) classifier to classify the faults of the wind turbine condition monitoring system. A stacking ensemble classifier integrates different classification models to enhance the model’s accuracy. We have used three classifiers, AdaBoost, K-nearest neighbors, and logistic regression, as base models to make output. The output of these three classifiers is used as input in the logistic regression classifier’s meta-model. To improve the data validity, SCADA data are first preprocessed by cleaning and removing any abnormal data. Next, the Pearson correlation coefficient was used to choose the input variables. The Stacking Ensemble classifier was trained using these parameters. The analysis demonstrates that the suggested method successfully identifies faults in wind turbines when applied to local 3 MW wind turbines. The proposed approach shows the potential for effective wind energy use, which could encourage the use of clean energy.https://www.mdpi.com/1424-8220/22/18/6955wind turbinesfault detectionstacking ensemble classifierAdaBoostK-nearest neighborslogistic regression |
| spellingShingle | Prince Waqas Khan Yung-Cheol Byun Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier Sensors wind turbines fault detection stacking ensemble classifier AdaBoost K-nearest neighbors logistic regression |
| title | Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier |
| title_full | Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier |
| title_fullStr | Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier |
| title_full_unstemmed | Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier |
| title_short | Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier |
| title_sort | multi fault detection and classification of wind turbines using stacking classifier |
| topic | wind turbines fault detection stacking ensemble classifier AdaBoost K-nearest neighbors logistic regression |
| url | https://www.mdpi.com/1424-8220/22/18/6955 |
| work_keys_str_mv | AT princewaqaskhan multifaultdetectionandclassificationofwindturbinesusingstackingclassifier AT yungcheolbyun multifaultdetectionandclassificationofwindturbinesusingstackingclassifier |