[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124799-en":3,"doc-seo-124799-105":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},124799,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Time Series Forecasting of Reactive Power Support from Smart Converters in SDN Using Machine Learning","Climate change drives the need for integrating more renewable and low‑carbon technologies into power systems, leading to increased operational variability in distribution networks. High penetration of distributed energy resources at low- and medium‑voltage levels introduces challenges for distribution system operators, especially reactive power management. Reactive power mismatches can cause voltage violations, so timely prediction enables better control decisions. This thesis proposes a machine learning time-series approach for reactive power forecasting in smart distribution networks.","Department of Electrical Engineering  \nTime Series Forecasting of Reactive Power Support from Smart Converters in SDN Using Machine Learning  \nMujtaba Aziz Candidate \\#: 2  \nMaster’s thesis in Electrical Engineering   ELE-3900   May 2023  \nACKNOWLEDGEMENT  \nFirst and foremost, I would like to express my sincerest gratitude to Allah Almighty for granting me the strength, perseverance, and opportunity to complete this thesis. Without His blessings and guidance, I would not have been able to reach this point.  \nI would like to extend my heartfelt appreciation to my supervisors, PhD researcher Raju Wagle and Professor Pawan Sharma, for their unwavering support, guidance, and encouragement throughout my academic journey. Raju Wagle was always accessible, even during his busy schedule he took out time to guide me. His invaluable insights, constructive criticism, and expertise have greatly contributed to the success of this thesis.  \nI am also deeply grateful to my company manager, Christian Murrilo, for providing me with the necessary resources and support that allowed me to pursue my master’s degree while working fulltime. His unwavering encouragement and trust in my abilities were instrumental in keeping me motivated and focused on my goals.  \nFurthermore, I would like to thank my parents for their unconditional love, support, and unwavering belief in my abilities. Their encouragement and constant prayers have been a source of inspiration for me. Additionally, I would like to express my gratitude to my friends for their continuous support and encouragement throughout this journey.  \nFinally, I would like to extend my thanks to all those who have contributed to my academic and personal growth, directly or indirectly. Your support, guidance, and encouragement have been instrumental in shaping my personality and helping me achieve my goals.  \nABSTRACT  \nThe climate changes in the last few years created a major need to integrate more renewable energy sources and other low-carbon technologies into the power system network. This causes to face more changes in the power system network, which are particularly visible in distribution power networks. A higher penetration of the distributed energy resources, installed at low-voltage and or medium-voltage levels, creates new challenges for distribution system operators. One of the important issues is to effectively manage the reactive power in a smart distribution network, as themismatch of the reactive power in a power system network can cause voltage violations in the network.  \nBy timely predicting the reactive power, a distribution system operator can make better decisions to avoid any voltage violations. Several conventional techniques like optimal power flow (OPF) control and droop control are used, which are highly dependent on grid models. But machine learning (ML) is an effective approach due to its capability of handling multiple variable data sets and its performance is being independent of grid constraints.  \nTherefore, in this thesis, we propose a machine learning-based approach for time series prediction of reactive power in a smart distribution network. After going through a literature review to find research gaps, a detailed methodology is discussed, highlighting tools used and how they impact on our objectives. Moving forward, a power flow analysis is performed to see the impact of reactive power on SDN. After acquiring all the data required for training algorithms, ML is implemented, and the results are also compared with the optimal power flow method. The results show that the predicted reactive power by the ML approach is very close to the OPF results. However, more improvements can be made by increasing the dataset and changing in the layers of ML algorithms.  \nTable of Contents  \nContents  \nABSTRACT..................................................................................................................................................4  \nABBREVIATIONS ...........","cbCaivNCHkoxFob4","https://ap.wps.com/l/cbCaivNCHkoxFob4","pdf",3360303,1,95,"English","en",105,"# Abstract\n# Introduction\n## Background and Motivation\n## Problem Statement\n## Research Objectives\n# Literature Review\n## Review on Smart Distribution Networks\n## Review on Power/Smart Converters\n# Methodology","[{\"question\":\"Why is reactive power forecasting important in a smart distribution network?\",\"answer\":\"Reactive power mismatches can lead to voltage violations in the network. Forecasting enables operators to make timely decisions to avoid such violations.\"},{\"question\":\"What conventional methods does the thesis compare against?\",\"answer\":\"The thesis discusses conventional techniques such as optimal power flow (OPF) control and droop control, which depend heavily on grid models.\"},{\"question\":\"How does the proposed machine learning approach improve prediction performance?\",\"answer\":\"The thesis trains machine learning models for time-series reactive power prediction and compares results with OPF. The findings show ML predictions are very close to OPF outputs, with further gains possible through larger datasets and modified model layers.\"}]","Time Series Forecasting of Reactive Power Support from Smart Converters in SDN Using Machine Learning | PDF",1785894722,239,{"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},"time-series-forecasting-of-reactive-power-support-from-smart-converters-in-sdn-using-machine-learning","",{"@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/time-series-forecasting-of-reactive-power-support-from-smart-converters-in-sdn-using-machine-learning/124799/",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","2026-08-05",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 reactive power forecasting important in a smart distribution network?","Question",{"text":75,"@type":76},"Reactive power mismatches can lead to voltage violations in the network. Forecasting enables operators to make timely decisions to avoid such violations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What conventional methods does the thesis compare against?",{"text":80,"@type":76},"The thesis discusses conventional techniques such as optimal power flow (OPF) control and droop control, which depend heavily on grid models.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed machine learning approach improve prediction performance?",{"text":84,"@type":76},"The thesis trains machine learning models for time-series reactive power prediction and compares results with OPF. The findings show ML predictions are very close to OPF outputs, with further gains possible through larger datasets and modified model layers.","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"]