TY  - EJOU
AU  - Li, Quan 
AU  - Zhou, Ziheng 

TI  - The Kalman Filter Design for MJS in Power System Based on Derandomization Technique
T2  - Energy Engineering

PY  - 2025
VL  - 122
IS  - 12
SN  - 1546-0118

AB  - This study considers the state estimation problem of the circuit breakers (CBs), solving for random abrupt changes that occurred in power systems. With the abrupt changes randomly occurring, it is represented in a Markov chain, and then the CBs can be considered as a Markov jump system (MJS). In these MJSs, the transition probabilities are obtained from historical statistical data of the random abrupt changes when the faults occurred. Considering that the traditional Kalman filter (KF) frameworks based on MJS only depend on the subsystem of MJS, but neglect the stochastic jump between different subsystems. This study utilized the derandomization technique which transforms the stochastic MJS to a deterministic system to introduce the stochastic mode jumping in MJS, in which the state is still in the same norm, and the Lyapunov function is derived to show the stability condition of the systems, which proved that the transformed deterministic system is more conservative than the original MJS mathematically. After that, the Kalman filter algorithm is designed for estimating the state of the CBs depending on the transformed deterministic system. With the help of the Kalman filter, the estimation performance is derived by the recursive state estimation algorithm for the CBs. Furthermore, a single machine infinite-bus (SMIB) power system and a three-bus large scale system are proposed as practical examples to validate the effectiveness of the proposed algorithm.
KW  - Markov jump system (MJS); circuit breaker (CB); Kalman filter; derandomization technique; power system; mode transition

DO  - 10.32604/ee.2025.068866