[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119072-en":3,"doc-seo-119072-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119072,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","A Review on Condition Monitoring of Wind Turbines Using Machine Learning Techniques","This document surveys up-to-date research on how machine learning techniques are applied to condition monitoring of wind turbines. It centers on classification approaches for fault identification, highlighting that most studies rely on Supervisory Control and Data Acquisition (SCADA) data. Neural networks, support vector machines, and decision trees are the most frequently used algorithms. The review also outlines future directions, including building more robust models for noisy data and extending ML toward prognosis for predicting forthcoming faults.","A Review on Condition Monitoring of Wind Turbines Using Machine Learning Techniques  \nDr. P. Muralidharan1, Gaurav thakur2, Shalini M3, Vikalp Sharma 4, Abootharmahmoodshakir5, and Anishkumar Dhablia6,  \n* Assistant Professor, School of Business and Management, Christ university yeshwanthpur campus Bangalore, India.  \n†Department of Civil Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun-248007, India.  \n‡Assistant Professor, Department of ECE,Prince Shri Venkateshwara Padmavathy Engineering College, Chennai – 127, India.  \n§Department of Computer Science & Engineering, IES College of Technology, Bhopal, Madhya Pradesh 462044 India.  \n**The Islamic university, Najaf, Iraq.  \n6Engineering Manager, AltimetrikIndia Pvt Ltd, Pune, Maharashtra, India [anishdhablia@gmail.com](anishdhablia@gmail.com).  \nAbstract. This document examines the most up-to-date research on the application of machine learning (ML) techniques in monitoring the conditions of wind turbines. The focus is on classification methods, which are used to identify different types of faults. The analysis revealed that the majority of the research utilizes Supervisory Control and Data Acquisition (SCADA) information, with neural networks, support vector machines, and decision trees being the most prevalent machine learning algorithms. Thereview also identifies several areas for future research, such as the development of more robust ML models that can handle noisy data and the  \nuse of ML methods for prognosis (predicting future faults) .  \nKeywords: Wind turbine, Renewable energy, Condition Monitoring,  \nMachine learning,  \n*Corresponding Authour :[muralidharan.p@christuniversity.in](muralidharan.p@christuniversity.in)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \n1. Introduction  \nDue in part to increased public investments and a growing awareness of climate change, we have observed swift technological progress in the field of renewable energy. As a result, the proportion of renewable energy sources, such as wind power, has been steadily on the rise when compared to conventional sources like fossil fuels. Wind energy, harnessed through turbines located either onshore (land-based) or offshore (at sea), has become a pivotal player in this transition. Offshore wind farms have gained prominence for several compelling reasons. Offshore wind farms can take advantage of the stronger and steadier winds at sea, which can lead to more reliable energy production [2] . Secondlyoffshore wind farms are less visible than onshore wind farms, which can mitigate potential conflicts of interest with residents [2].It is important to note that the maintenance costs of offshore wind turbines are significant. The cost of ensuring that these turbines operate optimally throughout their lifespan, which is typically 20 to 25 years, accounts for approximately 25% of the total cost of offshore wind farm installation. [3] .In this context, the crucial importance of condition monitoring (CM) becomes apparent, as it entails closely monitoring the various components of wind turbines to identify any deviations from normal operation that could indicate potential faults in the future. It is clear that the capacity to anticipate and rectify these faults proactively, through effective CM procedures, has the potential to substantially decrease the costs associated with Operation and Maintenance (O&M) . [4] . Traditionally, condition monitoring (CM) has been done by analyzing specific measurements and operational parameters, such as vibration, strain, temperature, and acoustic emissions. However, recent advances in sensor technology, signal processing, big data management, and machine learning (ML) have made it possible to use more integrated and comprehensive approaches to C","cbCaiauJHaBuvy2V","https://ap.wps.com/l/cbCaiauJHaBuvy2V","pdf",1612878,1,"English","en",105,"# Introduction\n## Renewable energy and the role of condition monitoring\n## ML-based approaches and review scope\n# Widely deployed methods for condition monitoring of wind turbines\n## Operations and maintenance context\n## Maintenance strategies (reactive, predictive, opportunistic)","[{\"question\":\"What does the review focus on regarding wind turbine condition monitoring?\",\"answer\":\"It focuses on applying machine learning to monitor wind turbine conditions, with emphasis on classification methods used to identify different fault types.\"},{\"question\":\"Which data source is most commonly used in the surveyed research?\",\"answer\":\"Most studies use Supervisory Control and Data Acquisition (SCADA) information for training and monitoring.\"},{\"question\":\"What machine learning algorithms are most prevalent in the literature reviewed?\",\"answer\":\"Neural networks, support vector machines, and decision trees are reported as the most common algorithms.\"}]","A Review on Condition Monitoring of Wind Turbines Using Machine Learning Techniques | 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does the review focus on regarding wind turbine condition monitoring?","Question",{"text":74,"@type":75},"It focuses on applying machine learning to monitor wind turbine conditions, with emphasis on classification methods used to identify different fault types.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which data source is most commonly used in the surveyed research?",{"text":79,"@type":75},"Most studies use Supervisory Control and Data Acquisition (SCADA) information for training and monitoring.",{"name":81,"@type":72,"acceptedAnswer":82},"What machine learning algorithms are most prevalent in the literature reviewed?",{"text":83,"@type":75},"Neural networks, support vector machines, and decision trees are reported as the most common 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