L. Durante, C. Burns, Mirjavad Hashemi Gavgani, R. Grabovickic, E. Seiter, T. Bujanovic
{"title":"高压输电线路对行波的响应","authors":"L. Durante, C. Burns, Mirjavad Hashemi Gavgani, R. Grabovickic, E. Seiter, T. Bujanovic","doi":"10.1109/LISAT58403.2023.10179537","DOIUrl":null,"url":null,"abstract":"Fault occurrence, location, and system performance information, based on the analysis of traveling waves is gaining importance as new levels of high frequency measurement equipment are being introduced to the power grid. In this work Continuous Wavelet Transformations with the complex Morlet wavelet analyzing functions was used to design a signal processing scheme to extract time and frequency characteristics from measured and recorded transient events that launched traveling waves on a 115 kV transmission system. The primary motivation was to extract baseline system performance information employing the traveling waves launched as a result of line re-energizations. This work is a proof of concept aimed extracting and baselining a system’s line specific characteristics or ‘fingerprints’ and determining if a useful level of fingerprint consistency exists for typical line energizations. Implementation of the proposed scheme on these specific signals, measured at 1.5 MHz sampling frequency, successfully extracted consistent fingerprints of the system’s time-frequency behavior. A database of these fingerprints could be used to determine a dynamic high-frequency model for the system as well as track changes in the physical system as the response changes in different source and system operational scenarios.","PeriodicalId":250536,"journal":{"name":"2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High Voltage Transmission Line Response to Traveling Waves\",\"authors\":\"L. Durante, C. Burns, Mirjavad Hashemi Gavgani, R. Grabovickic, E. Seiter, T. Bujanovic\",\"doi\":\"10.1109/LISAT58403.2023.10179537\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fault occurrence, location, and system performance information, based on the analysis of traveling waves is gaining importance as new levels of high frequency measurement equipment are being introduced to the power grid. In this work Continuous Wavelet Transformations with the complex Morlet wavelet analyzing functions was used to design a signal processing scheme to extract time and frequency characteristics from measured and recorded transient events that launched traveling waves on a 115 kV transmission system. The primary motivation was to extract baseline system performance information employing the traveling waves launched as a result of line re-energizations. This work is a proof of concept aimed extracting and baselining a system’s line specific characteristics or ‘fingerprints’ and determining if a useful level of fingerprint consistency exists for typical line energizations. Implementation of the proposed scheme on these specific signals, measured at 1.5 MHz sampling frequency, successfully extracted consistent fingerprints of the system’s time-frequency behavior. A database of these fingerprints could be used to determine a dynamic high-frequency model for the system as well as track changes in the physical system as the response changes in different source and system operational scenarios.\",\"PeriodicalId\":250536,\"journal\":{\"name\":\"2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/LISAT58403.2023.10179537\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LISAT58403.2023.10179537","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
High Voltage Transmission Line Response to Traveling Waves
Fault occurrence, location, and system performance information, based on the analysis of traveling waves is gaining importance as new levels of high frequency measurement equipment are being introduced to the power grid. In this work Continuous Wavelet Transformations with the complex Morlet wavelet analyzing functions was used to design a signal processing scheme to extract time and frequency characteristics from measured and recorded transient events that launched traveling waves on a 115 kV transmission system. The primary motivation was to extract baseline system performance information employing the traveling waves launched as a result of line re-energizations. This work is a proof of concept aimed extracting and baselining a system’s line specific characteristics or ‘fingerprints’ and determining if a useful level of fingerprint consistency exists for typical line energizations. Implementation of the proposed scheme on these specific signals, measured at 1.5 MHz sampling frequency, successfully extracted consistent fingerprints of the system’s time-frequency behavior. A database of these fingerprints could be used to determine a dynamic high-frequency model for the system as well as track changes in the physical system as the response changes in different source and system operational scenarios.