Open AccessRecent developments in load model parameter identification via ambient PMU signal
Author(s) -
Siyuan Guo,
Yunchen Jiang,
Daojun Chen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1983/1/012071
Subject(s) - identification (biology) , computer science , stability (learning theory) , transient (computer programming) , electric power system , signal (programming language) , field (mathematics) , system identification , mode (computer interface) , power (physics) , reliability engineering , control engineering , control theory (sociology) , engineering , data mining , control (management) , measure (data warehouse) , machine learning , artificial intelligence , botany , physics , mathematics , quantum mechanics , pure mathematics , biology , programming language , operating system
Load modelling has a significant impact on operation mode arrangement, transient stability analysis, small disturbance stability calculation and other aspects in power system. It is an important basis and issue for the decision-making of dispatching operation department. As a novel research field of measurement-based identification method, load model parameter identification via ambient PMU signal has gained some developments. This article reviews the current research status of ambient data-based load parameter identification, outlines its basic principle and typical framework, and expounds three technical routes. By summarizing the characteristics of various methods, the future research direction is prospected.