A Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation

Conference · Mon Sep 09 00:00:00 EDT 2019

DOI:https://doi.org/10.1109/PESGM40551.2019.8974102· OSTI ID:1603348

Akhlaghi, Shahrokh ^[1]; Zhou, Ning ^[1]; Huang, Zhenyu ^[2]

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1. State University of New York at Binghamton
2. BATTELLE (PACIFIC NW LAB)

Accurate information about dynamic states (such as rotor angle and speed of a synchronous machine) is important for monitoring and controlling power system rotor-angle stability. In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data; (ii) probability indexes, which quantify the likelihood of each estimation model, are determined at each time step using hypothesis testing based on the measurement innovation; (iii) the a posteriori estimate of states is obtained using the best-fix approach. The two-area four-machine system is used to evaluate the effectiveness of the proposed MMAKF approach. It is shown through the Monte-Carlo method that the estimation accuracy and robustness of the proposed approach is better than those from any individual filtering algorithm.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1603348
• Report Number(s): PNNL-SA-147878
• Country of Publication: United States
• Language: English

Related Subjects

multi-model adaptive Kalman Filtering
Power System Dynamic State Estimation
phasor measurement unit data