{"title":"Evaluation of Metrics to Detect High Impedance Faults Using Real Current Signals","authors":"G. N. Lopes, T. S. Menezes, J. Vieira","doi":"10.1109/ICHQP53011.2022.9808554","DOIUrl":null,"url":null,"abstract":"High Impedance Faults (HIFs) occur due to the contact between an energized conductor and a high impedance surface. Due to the potential dangers caused by HIFs, several methods have been proposed for their identification over the years. Nonetheless, there is still no fully effective technique for their identification. Therefore, this paper proposes indices for evaluating metrics extracted from HIF signals. The goal is to point out the advantages and drawbacks of the selected metrics, aiming to support researchers in developing more effective identification methods. The analyses were performed by using actual HIF signals on soil and vegetation, and the proposed indices are roughness, local inclination, and the global tendency of the metrics. The results revealed relevant characteristics of each metric that can be employed for identifying HIFs, supporting the development of new detection methods.","PeriodicalId":249133,"journal":{"name":"2022 20th International Conference on Harmonics & Quality of Power (ICHQP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 20th International Conference on Harmonics & Quality of Power (ICHQP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICHQP53011.2022.9808554","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
High Impedance Faults (HIFs) occur due to the contact between an energized conductor and a high impedance surface. Due to the potential dangers caused by HIFs, several methods have been proposed for their identification over the years. Nonetheless, there is still no fully effective technique for their identification. Therefore, this paper proposes indices for evaluating metrics extracted from HIF signals. The goal is to point out the advantages and drawbacks of the selected metrics, aiming to support researchers in developing more effective identification methods. The analyses were performed by using actual HIF signals on soil and vegetation, and the proposed indices are roughness, local inclination, and the global tendency of the metrics. The results revealed relevant characteristics of each metric that can be employed for identifying HIFs, supporting the development of new detection methods.