[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121302-en":3,"doc-seo-121302-105":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":20,"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},121302,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Trustworthy machine learning in smart grids","More than a decade after its introduction, the smart grid concept remains central to the industry’s ongoing digital transformation. A smart grid enables bidirectional electricity and data flows, detecting and responding proactively to demand changes through digital communications. Modern smart grids also require self-healing capabilities that restore service and reduce outage duration to limit cascading failures. This dissertation integrates machine learning methods to enhance self-healing via fault prediction, evaluating both accuracy and trustworthiness. Trust is assessed through robustness to faults and adversarial attacks and through interpretability of the prediction systems.","Repository Istituzionale dei Prodotti della Ricerca del Politecnico di Bari  \nTrustworthy machine learning in smart grids  \nThis is a PhD Thesis  \nOriginal Citation:  \nTrustworthy machine learning in smart grids / Nazary, Fatemeh. -ELETTRONICO. - (2023) . [10 .60576/poliba/iris/nazaryfatemeh_phd2023]  \nAvailability:  \nThis version is available at [http://hdl.handle.net/11589/248980 since: 2023-03-24](http://hdl.handle.net/11589/248980 since: 2023-03-24)  \nPublished version  \nDOI:10.60576/poliba/iris/nazary-fatemeh_phd2023  \nPublisher: Politecnico di Bari Terms of use:  \n(Article begins on next page)  \nDEPARTMENT OF ELECTRICAL AND INFORMATION ENGINEERING ELECTRICAL AND INFORMATION ENGINEERING PH . D. PROGRAMSSD: ING-INF/05 INFORMATION PROCESSING SYSTEMS  \nFINAL DISSERTATION  \nTRUSTWORTHY MACHINE LEARNING IN  \nSMART GRIDS  \nBy:  \nFatemeh Nazary  \nAcademic Supervisors:  \nProf. Carmelo Ardito Prof. Eugenio Di Sciascio  \nCompany Supervisor:  \nEng. Gianluca Sapienza  \nCoordinator of Ph.D Program:  \nProf. Mario Carpentieri  \nCourse n◦ 35, 01/11/2019-31/12/2022  \n[This work has been funded by e-distribuzione S.p.A. com](This work has been funded by e-distribuzione S.p.A. com)pany, Italy.  \nDedicated to my love (Yashar), my dear mother, and my dear brother (Amir) .  \nAcknowledgements  \nIn the beginning, I would like to express my gratitude and appreciation to my supervisor Prof. Carmelo Ardito. I thank him for helping and supporting me whenever I needed it. I thank him for building the trust on me and give me the freedom to do my research.  \nI would also like to thank my other supervisors Prof. Eugenio Di Sciascio and Eng. Gianluca Sapienza for their support from the university and company sides.  \nI wish to express my gratitude to Prof. Tommaso Di Noia whom I had his support during my Ph.D. period.  \nI am particularly grateful to my husband Yashar, who supported meand inspired me scientifically and spiritually during this period. I love you Yashar and thank you for everything, especially your love and kindness.  \nFurthermore, I thank Eng. Luca Delli Carpini, the Engineer in edistribuzione Smart Grid Lab, explained the overall picture to us in Milan.  \nLast but not the least, I would like to thank my first and forever friend my mother and my brother who has always been supportive of my family. I would like to thank my grandpa who was my father and supporter throughout my life and left me alone 6 years ago. I would also like to thank my dear mother-in-law for her support; you are always with us.  \nAbstract  \nMOre than a decade after its introduction, the concept of a “smart  \ngrid” remains essential to the industry’s ongoing digital trans  \nformation. A smart grid (SG) is an electricity network that enables the bidirectional flow of electricity and data and can detect, react to, and proactively address changes in demand and a variety of other concerns, all through the use of digital communications technology. Modern SGs designed for the 21st century are required to have self-healing capabilities, which are characterized by the capacity to automatically restore and recover the interruption of energy in the grid and to shorten the interruption period for customers, thereby decreasing the likelihood of amore severe disaster, such as one caused by a cascading effect.  \nA wide range of disciplines, including computer science, electrical engineering, signal processing, statistics, artificial intelligence, and machine learning, have been applied to the study of automatic fault prediction tasks over the past years. This dissertation focuses on the integration of machine learning-based techniques to improve the self-healing capabilities of SGs and examines these ML approaches not only from the standpoint of fault prediction accuracy but also their trustworthiness. Among the numerous facets of trust, this study focuses on the robustness (against faults and adversarial attacks) and interpretability of the proposed fault prediction systems.  \nThis ","cbCaigK8sy5NvjCH","https://ap.wps.com/l/cbCaigK8sy5NvjCH","pdf",4877808,1,129,"English","en",105,"# Abstract\n## Smart grid and self-healing requirements\n## Machine learning for fault prediction\n## Trustworthiness: robustness and interpretability","[{\"question\":\"What problem does the dissertation address in smart grids?\",\"answer\":\"It targets improving smart grid self-healing by using machine-learning-based fault prediction to restore service and reduce outage duration, lowering the risk of cascading failures.\"},{\"question\":\"How does the work evaluate the proposed machine learning approaches?\",\"answer\":\"It assesses fault prediction accuracy and, additionally, trustworthiness. Trustworthiness is examined via robustness against faults and adversarial attacks, and via interpretability of the prediction systems.\"},{\"question\":\"What aspects of “trust” are emphasized in this research?\",\"answer\":\"The study focuses on robustness (including resistance to adversarial attacks) and interpretability, rather than only performance metrics.\"}]","Trustworthy machine learning in smart grids | PDF",1785734976,325,{"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},"trustworthy-machine-learning-in-smart-grids","",{"@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/trustworthy-machine-learning-in-smart-grids/121302/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the dissertation address in smart grids?","Question",{"text":75,"@type":76},"It targets improving smart grid self-healing by using machine-learning-based fault prediction to restore service and reduce outage duration, lowering the risk of cascading failures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the work evaluate the proposed machine learning approaches?",{"text":80,"@type":76},"It assesses fault prediction accuracy and, additionally, trustworthiness. Trustworthiness is examined via robustness against faults and adversarial attacks, and via interpretability of the prediction systems.",{"name":82,"@type":73,"acceptedAnswer":83},"What aspects of “trust” are emphasized in this research?",{"text":84,"@type":76},"The study focuses on robustness (including resistance to adversarial attacks) and interpretability, rather than only performance metrics.","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"]