A Comparative Study of Bayesian based filters for Dynamic State Estimation in Power Systems
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Date
2021
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Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Dynamic state estimation of a power system is the first prerequisite for control and stability prediction under transient conditions. Among the states of synchronous machine, precise, accurate, and timely information about rotor angle and speed deviation can be useful to enhance power system reliability and stability. In this paper, the popular variants of Kalman filter are used to estimate the main states of synchronous generators of an SMIB and IEEE 3-generators 9-bus test systems. A case study using a simple power system model is presented to illustrate the comparison between effectiveness of proposed approaches. © 2021 IEEE.
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Keywords
CKF, DSE, EKF, PMU, UKF
Citation
2021 IEEE International Power and Renewable Energy Conference, IPRECON 2021, 2021, Vol., , p. -