{"title":"利用库普曼算子进行数据驱动的瞬态稳定性分析","authors":"","doi":"10.1016/j.ijepes.2024.110307","DOIUrl":null,"url":null,"abstract":"<div><div>We present data-driven methods for power system transient stability analysis using a unit eigenfunction of the Koopman operator. We show that the Koopman eigenfunction with unit eigenvalue can identify the region of attraction of the post-fault stable equilibrium. We then leverage this property to estimate the critical clearing time of a fault. We provide two data-driven methods to estimate said eigenfunction; the first method utilizes time averages over long trajectories, and the second method leverages nonparametric learning of system dynamics over reproducing kernel Hilbert spaces with short bursts of state propagation. Our methods do not require explicit knowledge of the power system model, but require a simulator that can propagate states through the power system dynamics. Numerical experiments on three power system examples demonstrate the efficacy of our method.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-driven transient stability analysis using the Koopman operator\",\"authors\":\"\",\"doi\":\"10.1016/j.ijepes.2024.110307\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>We present data-driven methods for power system transient stability analysis using a unit eigenfunction of the Koopman operator. We show that the Koopman eigenfunction with unit eigenvalue can identify the region of attraction of the post-fault stable equilibrium. We then leverage this property to estimate the critical clearing time of a fault. We provide two data-driven methods to estimate said eigenfunction; the first method utilizes time averages over long trajectories, and the second method leverages nonparametric learning of system dynamics over reproducing kernel Hilbert spaces with short bursts of state propagation. Our methods do not require explicit knowledge of the power system model, but require a simulator that can propagate states through the power system dynamics. Numerical experiments on three power system examples demonstrate the efficacy of our method.</div></div>\",\"PeriodicalId\":50326,\"journal\":{\"name\":\"International Journal of Electrical Power & Energy Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Electrical Power & Energy Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0142061524005301\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Electrical Power & Energy Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0142061524005301","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Data-driven transient stability analysis using the Koopman operator
We present data-driven methods for power system transient stability analysis using a unit eigenfunction of the Koopman operator. We show that the Koopman eigenfunction with unit eigenvalue can identify the region of attraction of the post-fault stable equilibrium. We then leverage this property to estimate the critical clearing time of a fault. We provide two data-driven methods to estimate said eigenfunction; the first method utilizes time averages over long trajectories, and the second method leverages nonparametric learning of system dynamics over reproducing kernel Hilbert spaces with short bursts of state propagation. Our methods do not require explicit knowledge of the power system model, but require a simulator that can propagate states through the power system dynamics. Numerical experiments on three power system examples demonstrate the efficacy of our method.
期刊介绍:
The journal covers theoretical developments in electrical power and energy systems and their applications. The coverage embraces: generation and network planning; reliability; long and short term operation; expert systems; neural networks; object oriented systems; system control centres; database and information systems; stock and parameter estimation; system security and adequacy; network theory, modelling and computation; small and large system dynamics; dynamic model identification; on-line control including load and switching control; protection; distribution systems; energy economics; impact of non-conventional systems; and man-machine interfaces.
As well as original research papers, the journal publishes short contributions, book reviews and conference reports. All papers are peer-reviewed by at least two referees.