Open AccessMeasurement‐based correlation approach for power system dynamic response estimation
Author(s) -
Bai Feifei,
Liu Yong,
Liu Yilu,
Sun Kai,
Bhatt Navin,
Del Rosso Alberto,
Farantatos Evangelos,
Wang Xiaoru
Publication year - 2015
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2014.1013
Subject(s) - electric power system , robustness (evolution) , phasor , correlation , computer science , system identification , phasor measurement unit , system dynamics , field (mathematics) , control theory (sociology) , power (physics) , data mining , control (management) , mathematics , artificial intelligence , biochemistry , physics , chemistry , geometry , quantum mechanics , gene , measure (data warehouse) , pure mathematics
Understanding power system dynamics is essential for online stability assessment and control applications. Global positioning system‐synchronised phasor measurement units and frequency disturbance recorders (FDRs) make power system dynamics visible and deliver an accurate picture of the overall operation condition to system operators. However, in the actual field implementations, some measurement data can be inaccessible for various reasons, for example, most notably failure of communication. In this study, a measurement‐based approach is proposed to estimate the missing power system dynamics. Specifically, a correlation coefficient index is proposed to describe the correlation relationship between different measurements. Then, the auto‐regressive with exogenous input identification model is employed to estimate the missing system dynamic response. The US Eastern Interconnection is utilised in this study as a case study. The robustness of the correlation approach is verified by a wide variety of case studies as well. Finally, the proposed correlation approach is applied to the real FDR data for power system dynamic response estimation. The results indicate that the correlation approach could help select better input locations and thus improve the response estimation accuracy.