Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement
Abstract The research study objective seeks to improve the efficiency of wind turbines using state-of-the-art techniques in the domain of ML, making wind energy the key player in fashioning a favorable future. Wind Turbine Health Monitoring (WTHM) is typically achieved through either vibration analy...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85563-5 |
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| author | Sekar Kidambi Raju Muthusamy Periyasamy Amel Ali Alhussan Subhash Kannan Srikanth Raghavendran El-Sayed M. El-kenawy |
| author_facet | Sekar Kidambi Raju Muthusamy Periyasamy Amel Ali Alhussan Subhash Kannan Srikanth Raghavendran El-Sayed M. El-kenawy |
| author_sort | Sekar Kidambi Raju |
| collection | DOAJ |
| description | Abstract The research study objective seeks to improve the efficiency of wind turbines using state-of-the-art techniques in the domain of ML, making wind energy the key player in fashioning a favorable future. Wind Turbine Health Monitoring (WTHM) is typically achieved through either vibration analysis or by using Supervisory Control and Data Acquisition (SCADA) data of wind turbines, wherein conventional fault pattern identification is a time-consuming, guesswork process. This work proposed an intelligent automated approach to early fault detection through the implementation of the HARO (Huber Adam Regression Optimizer) model, which combines Transformer networks with Lasso Regression and the Adam optimizer. HARO is the proposed model to traditional models, including the Huber and Automatic Relevance Determination (ARD) Regressors, since Transformers are capable of learning the patterns of the sensors. The overall analysis of the results obtained illustrated that the Housing Asset Repair Optimization tool had reduced downtime while enhancing, at the same time, the accuracy of the prediction of future faults to enable the right-time planning for maintenance activities. This aspect enhances the learning process, minimizing human input and hence slightly lessens the efficiency of the turbines in HARO. The study further calls for collective research practice amongst researchers, practitioners and policymakers to address emerging issues in wind power maintenance to avoid duplication and continuously keep improving on the strategies employed towards the maintenance of wind energy. This paper also shows that ML has the possibility of improving wind turbine dependability and establishing wind energy as an essential component of renewable energy systems. |
| format | Article |
| id | doaj-art-74a004cd25284e6a8bed0cf809ad85a2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-74a004cd25284e6a8bed0cf809ad85a22025-01-12T12:22:50ZengNature PortfolioScientific Reports2045-23222025-01-0115112410.1038/s41598-025-85563-5Machine learning boosts wind turbine efficiency with smart failure detection and strategic placementSekar Kidambi Raju0Muthusamy Periyasamy1Amel Ali Alhussan2Subhash Kannan3Srikanth Raghavendran4El-Sayed M. El-kenawy5School of Computing, SASTRA Deemed UniversityDepartment of Cyber Security Paavai Engineering College (AutonomousDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityK. Ramakrishnan College of Engineering (Autonomous)Department of mathematics, SASHE, SASTRA Deemed UniversityDepartment of Communications and Electronics, Delta Higher Institute of Engineering and TechnologyAbstract The research study objective seeks to improve the efficiency of wind turbines using state-of-the-art techniques in the domain of ML, making wind energy the key player in fashioning a favorable future. Wind Turbine Health Monitoring (WTHM) is typically achieved through either vibration analysis or by using Supervisory Control and Data Acquisition (SCADA) data of wind turbines, wherein conventional fault pattern identification is a time-consuming, guesswork process. This work proposed an intelligent automated approach to early fault detection through the implementation of the HARO (Huber Adam Regression Optimizer) model, which combines Transformer networks with Lasso Regression and the Adam optimizer. HARO is the proposed model to traditional models, including the Huber and Automatic Relevance Determination (ARD) Regressors, since Transformers are capable of learning the patterns of the sensors. The overall analysis of the results obtained illustrated that the Housing Asset Repair Optimization tool had reduced downtime while enhancing, at the same time, the accuracy of the prediction of future faults to enable the right-time planning for maintenance activities. This aspect enhances the learning process, minimizing human input and hence slightly lessens the efficiency of the turbines in HARO. The study further calls for collective research practice amongst researchers, practitioners and policymakers to address emerging issues in wind power maintenance to avoid duplication and continuously keep improving on the strategies employed towards the maintenance of wind energy. This paper also shows that ML has the possibility of improving wind turbine dependability and establishing wind energy as an essential component of renewable energy systems.https://doi.org/10.1038/s41598-025-85563-5Wind turbine health monitoringMachine learningOptimizing wind energy generationFailure detectionStrategic wind turbine placement |
| spellingShingle | Sekar Kidambi Raju Muthusamy Periyasamy Amel Ali Alhussan Subhash Kannan Srikanth Raghavendran El-Sayed M. El-kenawy Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement Scientific Reports Wind turbine health monitoring Machine learning Optimizing wind energy generation Failure detection Strategic wind turbine placement |
| title | Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement |
| title_full | Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement |
| title_fullStr | Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement |
| title_full_unstemmed | Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement |
| title_short | Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement |
| title_sort | machine learning boosts wind turbine efficiency with smart failure detection and strategic placement |
| topic | Wind turbine health monitoring Machine learning Optimizing wind energy generation Failure detection Strategic wind turbine placement |
| url | https://doi.org/10.1038/s41598-025-85563-5 |
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