{"title":"Engineering fault intelligent monitoring system based on Internet of Things and GIS","authors":"Xiaoxing Su","doi":"10.1515/nleng-2022-0322","DOIUrl":null,"url":null,"abstract":"Abstract The power grid (referred to as PG for convenience) structure is becoming increasingly complex. Aiming at the problem that it is difficult for traditional PG monitoring methods to accurately detect PG faults, an intelligent PG fault monitoring system is constructed using Internet of Things (IoT) and geographic information system (GIS) to improve the effectiveness of fault monitoring. The sensor equipment is used to collect the current information in the circuit, and the change of induced current is used to judge the cause of the fault, and the fault information is transmitted to the monitoring center through communication technology. The staff can directly locate the geographical location of the fault in the visual interface. One hundred overhead lines of Xianyang Power Supply Company are selected for analysis, and the performance of the traditional PG monitoring method and intelligent PG fault monitoring system is compared. The average fault detection accuracy of the traditional PG monitoring method and the system proposed in this article is 72.0 and 94.8%, respectively. The average fault location accuracy of the traditional PG monitoring method and this system is 80.8 and 96.5%, respectively. The intelligent monitoring system of PG fault based on IoT and GIS has high accuracy in PG fault detection and fault location, which can improve the effectiveness of fault monitoring.","PeriodicalId":37863,"journal":{"name":"Nonlinear Engineering - Modeling and Application","volume":"57 1","pages":"0"},"PeriodicalIF":2.4000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nonlinear Engineering - Modeling and Application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/nleng-2022-0322","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract The power grid (referred to as PG for convenience) structure is becoming increasingly complex. Aiming at the problem that it is difficult for traditional PG monitoring methods to accurately detect PG faults, an intelligent PG fault monitoring system is constructed using Internet of Things (IoT) and geographic information system (GIS) to improve the effectiveness of fault monitoring. The sensor equipment is used to collect the current information in the circuit, and the change of induced current is used to judge the cause of the fault, and the fault information is transmitted to the monitoring center through communication technology. The staff can directly locate the geographical location of the fault in the visual interface. One hundred overhead lines of Xianyang Power Supply Company are selected for analysis, and the performance of the traditional PG monitoring method and intelligent PG fault monitoring system is compared. The average fault detection accuracy of the traditional PG monitoring method and the system proposed in this article is 72.0 and 94.8%, respectively. The average fault location accuracy of the traditional PG monitoring method and this system is 80.8 and 96.5%, respectively. The intelligent monitoring system of PG fault based on IoT and GIS has high accuracy in PG fault detection and fault location, which can improve the effectiveness of fault monitoring.
期刊介绍:
The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.