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Power system dynamic state estimation considering correlation of measurement error from PMU and SCADA
Author(s) -
Lu Zigang,
Wei Zhig,
Sun Yonghui
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4726
Subject(s) - scada , phasor measurement unit , observational error , kalman filter , covariance matrix , control theory (sociology) , electric power system , computer science , system of measurement , units of measurement , power (physics) , phasor , engineering , statistics , algorithm , mathematics , control (management) , quantum mechanics , astronomy , artificial intelligence , electrical engineering , physics
Summary It is well known that measurements from phasor measurement unit (PMU) or supervisory control and data acquisition (SCADA) are not generally independent. Since the correlation of measurement error is a very representative feature of the actual measurement system, traditional assumptions on error independency are not adequate. In this paper, taking the correlation of measurement error of both PMU and SCADA measurements into consideration, a novel correlated extended Kalman filter (CEKF) is proposed. The actual measurement configurations are analyzed with the consideration of measurement error transfer characteristics. Then, the modified measurement error covariance matrix is calculated by using the point estimation method, which will replace the traditional diagonal variance matrix. At last, IEEE 14‐bus system and 57‐bus system are provided to illustrate the effectiveness and superiority of the method, respectively.