{"title":"Modal Analysis-Based Analytical Method for Frequency Estimation During Inertia Response Stage of Power Systems","authors":"Tiezhu Wang;Shicong Ma;Shanshan Wang;Weilin Hou;Juncheng Gao;Jianbo Guo;Xiaoxin Zhou","doi":"10.1109/JETCAS.2023.3291455","DOIUrl":null,"url":null,"abstract":"With the increasing adoption of renewable energy and HVDC transmission systems, the power system may experience large power fluctuations due to HVDC faults, potentially causing the rate of change of frequency (RoCoF) or frequency deviation limit to be exceeded during the inertia response phase. The system’s ability to withstand these disturbances primarily depends on the amount of system inertia, making it crucial to accurately estimate the effective inertia. The traditional power system frequency analysis commonly employs the system frequency response (SFR) model based on the center of inertia (COI), which does not account for the spatial differences in frequency, and consequently results in reduced accuracy. To address this issue, this paper proposes a modal analysis-based analytical method (MAAM) for analyzing the system frequency characteristics during the inertia response phase. The proposed method retains the frequency dynamics of all generator rotors in the system and more accurately reflects the spatial variation characteristics of frequency compared to the COI model. This paper also introduces the concept of the effective inertia of the system, along with its calculation method. The proposed method is validated using the IEEE 2-region 4-generator system and New England 68 bus system.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10171800/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
With the increasing adoption of renewable energy and HVDC transmission systems, the power system may experience large power fluctuations due to HVDC faults, potentially causing the rate of change of frequency (RoCoF) or frequency deviation limit to be exceeded during the inertia response phase. The system’s ability to withstand these disturbances primarily depends on the amount of system inertia, making it crucial to accurately estimate the effective inertia. The traditional power system frequency analysis commonly employs the system frequency response (SFR) model based on the center of inertia (COI), which does not account for the spatial differences in frequency, and consequently results in reduced accuracy. To address this issue, this paper proposes a modal analysis-based analytical method (MAAM) for analyzing the system frequency characteristics during the inertia response phase. The proposed method retains the frequency dynamics of all generator rotors in the system and more accurately reflects the spatial variation characteristics of frequency compared to the COI model. This paper also introduces the concept of the effective inertia of the system, along with its calculation method. The proposed method is validated using the IEEE 2-region 4-generator system and New England 68 bus system.
期刊介绍:
The IEEE Journal on Emerging and Selected Topics in Circuits and Systems is published quarterly and solicits, with particular emphasis on emerging areas, special issues on topics that cover the entire scope of the IEEE Circuits and Systems (CAS) Society, namely the theory, analysis, design, tools, and implementation of circuits and systems, spanning their theoretical foundations, applications, and architectures for signal and information processing.