[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124333-en":3,"doc-seo-124333-105":30,"detail-sidebar-cat-0-en-105":91},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124333,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Probabilistic machine learning aided transformer lifetime prediction framework for wind energy systems - Paper summary","Accurate lifetime prediction of grid transformers supporting renewable energy is difficult because available datasets are often limited. For wind energy, stochastic ageing is intensified by wind and weather intermittency, while detailed power-topology stress modelling demands high computational cost and long simulations. A probabilistic machine-learning lifetime prediction model is proposed using thermal modelling and ageing analysis, compared against wind-farm synthetic simulations and validated with real data. Results show 0.47% median error and 80% prediction interval errors within 6%–7%, alongside reduced time and memory requirements. Sensitivity studies quantify effects of overloading strategies, thermal constants and wind-farm geography.","Electrical Power and Energy Systems 153 (2023) 109352  \n| Probabilistic machine learning aided transformer lifetime prediction framework for wind energy systems\u003Cbr>Jose I. Aizpurua a,b,∗, Rafael Peña-Alzola c, Jon Olano a, Ibai Ramirez a, Iker Lasad, Luis del Rio d, Tomislav Dragicevic e\u003Cbr>a Mondragon University, Electronics & Computer Science Department, Arrasate, Spain b Ikerbasque, Basque Foundation for Science, Bilbao, Spain\u003Cbr>c University of Strathclyde, Electronic & Electrical Engineering Department, Glasgow, UK d Ormazabal Corporate Technology, Amorebieta-Etxano, Spain\u003Cbr>e Technical University of Denmark, Department of Wind and Energy Systems, Lyngby, Denmark |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords: Transformer\u003Cbr>Wind energy Reliability Machine learning Surrogate modelling Power curve |  | Accurate lifetime prediction of transformers operated in power grids with renewable energy systems is a challenging task because it requires a large amount of data that is not usually available. In the case of wind energy, this complexity is intensified with the stochastic ageing process influenced by the intermittency of the wind and weather conditions. Existing models make use of detailed power topologies to evaluate transformer stress profiles and associated degradation. However, this modelling approach requires high computational resources and long simulation times. In this context, this paper presents a lifetime prediction model for transformers designed through probabilistic machine learning, thermal modelling and ageing analysis. The proposed model is compared with synthetic wind-to-power detailed simulations of a wind farm and validated with real data. The lifetime prediction is evaluated with different mission profile estimates and results show that the accuracy of the probabilistic machine learning model is very high, with an error of 0.47% for the median value and 80% prediction interval errors within 6%–7% with respect to observations. Moreover, thereis a substantial reduction in the simulation time and memory requirements when compared to the synthetic model. A detailed sensitivity analysis demonstrates the influence on transformer ageing of different overloading strategies, thermal constants and the geographic location of the wind farm. |  |\n\n1. Introduction  \nRenewable energy sources (RESs) play a key role in the transition towards a decarbonized economy, and their reliable and efficient integration into the power grid is crucial for the safe operation of power and energy systems. Wind energy (WE) is the most mature RES, with a total of 837 GW of wind power installed worldwide [1]. The expected lifetime of a WE plant is around 15–20 years, and the health status of WE components along with operation and maintenance costs (O&M) drive lifetime extension decisions [2]. Prognostics and health management (PHM) solutions reduce O&M costs and support lifetime extension of assets through e.g. early detection of anomalies [3], health state diagnostics [4], prediction of remaining useful life (RUL) [5] and condition-based maintenance solutions [6].  \nDifferent PHM and reliability models have been proposed mainly to evaluate the lifetime of mechanical components of WE systems [7]. However, PHM for the electrical system could play an important role  \nin the O&M cost reduction. In particular, transformers are key components that enable the integration of WE into the grid [8]. There are different factors that influence transformer degradation [9]. Wellestablished transformer O&M practices include dissolved gas analysis and offline testing as in [10]. Insulation degradation is the most penalizing phenomenon that can cause power outages [11]. Transformer insulation lifetime depends on the hottest-spot temperature (HST), which changes with the transformer loading and ambient temperature. In turn, the transformer loading and ambient temperature are specific to the operation loc","cbCaimvhg3hhzrvd","https://ap.wps.com/l/cbCaimvhg3hhzrvd","pdf",3103577,1,15,"English","en",105,"# Introduction\n## Related work","[{\"question\":\"Why is transformer lifetime prediction for wind-energy grids challenging?\",\"answer\":\"It requires large datasets that are usually unavailable, and wind and weather intermittency makes stochastic ageing more complex.\"},{\"question\":\"How does the proposed framework estimate transformer lifetime?\",\"answer\":\"It combines probabilistic machine learning with thermal modelling and an ageing analysis to predict lifetime under different mission profile estimates.\"},{\"question\":\"What performance and computational benefits does the model achieve?\",\"answer\":\"It attains a 0.47% median error, with 80% prediction interval errors within 6%–7% versus observations, while substantially reducing simulation time and memory compared with detailed synthetic modelling.\"}]","Probabilistic machine learning aided transformer lifetime prediction framework for wind energy systems - Paper summary | PDF",1785821655,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"probabilistic-machine-learning-aided-transformer-lifetime-prediction-framework-for-wind-energy-systems-paper-summary","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/probabilistic-machine-learning-aided-transformer-lifetime-prediction-framework-for-wind-energy-systems-paper-summary/124333/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is transformer lifetime prediction for wind-energy grids challenging?","Question",{"text":75,"@type":76},"It requires large datasets that are usually unavailable, and wind and weather intermittency makes stochastic ageing more complex.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed framework estimate transformer lifetime?",{"text":80,"@type":76},"It combines probabilistic machine learning with thermal modelling and an ageing analysis to predict lifetime under different mission profile estimates.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance and computational benefits does the model achieve?",{"text":84,"@type":76},"It attains a 0.47% median error, with 80% prediction interval errors within 6%–7% versus observations, while substantially reducing simulation time and memory compared with detailed synthetic modelling.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]