{"title":"识别配送网络模型的进化计算方法","authors":"","doi":"10.1016/j.engappai.2024.109184","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we present a novel methodology for generating low-voltage distribution network models. The methodology is based on leveraging the existing knowledge of the network topology and a comprehensive catalog of the conductors that are installed in each segment of the grid. By using genetic algorithms and data obtained from the smart-meters in the network, the proposed method can produce highly accurate network models. The effectiveness of this methodology has been confirmed by extensive simulation studies achieving errors of less than 0.4 V in the estimation of nodal voltages in scenarios without measurements noise and on the order of the standard deviation of the error considered in the measurements when disturbances are added to the problem.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0952197624013423/pdfft?md5=4357c5d67d579220e1a61a0406cffed7&pid=1-s2.0-S0952197624013423-main.pdf","citationCount":"0","resultStr":"{\"title\":\"An evolutionary computational approach for the identification of distribution networks models\",\"authors\":\"\",\"doi\":\"10.1016/j.engappai.2024.109184\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper, we present a novel methodology for generating low-voltage distribution network models. The methodology is based on leveraging the existing knowledge of the network topology and a comprehensive catalog of the conductors that are installed in each segment of the grid. By using genetic algorithms and data obtained from the smart-meters in the network, the proposed method can produce highly accurate network models. The effectiveness of this methodology has been confirmed by extensive simulation studies achieving errors of less than 0.4 V in the estimation of nodal voltages in scenarios without measurements noise and on the order of the standard deviation of the error considered in the measurements when disturbances are added to the problem.</p></div>\",\"PeriodicalId\":50523,\"journal\":{\"name\":\"Engineering Applications of Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0952197624013423/pdfft?md5=4357c5d67d579220e1a61a0406cffed7&pid=1-s2.0-S0952197624013423-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0952197624013423\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624013423","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
An evolutionary computational approach for the identification of distribution networks models
In this paper, we present a novel methodology for generating low-voltage distribution network models. The methodology is based on leveraging the existing knowledge of the network topology and a comprehensive catalog of the conductors that are installed in each segment of the grid. By using genetic algorithms and data obtained from the smart-meters in the network, the proposed method can produce highly accurate network models. The effectiveness of this methodology has been confirmed by extensive simulation studies achieving errors of less than 0.4 V in the estimation of nodal voltages in scenarios without measurements noise and on the order of the standard deviation of the error considered in the measurements when disturbances are added to the problem.
期刊介绍：
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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