Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

Journal Article · Wed Feb 20 04:00:00 EST 2019 · IEEE Access

DOI:https://doi.org/10.1109/ACCESS.2019.2900228· OSTI ID:1592007

Li, Yang ^[1]; Li, Jing ^[2]; Qi, Junjian ^[3]; Chen, Liang ^[4]

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1. Northeast Electric Power Univ., Jilin (China); University of Central Florida
2. Northeast Electric Power Univ., Jilin (China)
3. Univ. of Central Florida, Orlando, FL (United States)
4. State Grid Heibei Economic Research Inst., Shijiazhuang (China)

The Kalman-type filtering techniques including cubature Kalman filter (CKF) does not work well in non-Gaussian environments, especially in the presence of outliers. To solve this problem, Huber's M-estimation-based robust CKF (RCKF) is proposed for synchronous machines by combining the Huber's M-estimation theory with the classical CKF, which is capable of coping with the deterioration in performance and discretization of tracking curves when measurement noise statistics deviate from the prior noise statistics. The proposed RCKF algorithm has good adaptability to unknown measurement noise statistics characteristics including non-Gaussian measurement noise and outliers. The simulation results on the WSCC 3-machine 9-bus system and New England 16-machine 68-bus system verify the effectiveness of the proposed method and its advantage over the classical CKF.

• Research Organization: Univ. of Central Florida, Orlando, FL (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• Grant/Contract Number: EE0007327
• OSTI ID: 1592007
• Journal Information: IEEE Access, Journal Name: IEEE Access Vol. 7; ISSN 2169-3536
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
Dynamic state estimation
M-estimation theory
PMU data
cubature Kalman filter
non-Gaussian noise
outliers
synchronous machines
unknown noise statistics