Tuning of extended kalman filter for power systems using two lbest particle swarm optimization
Publication details: Haryana International Science Press 2021Edition: Vol.13(1), Jan-JuneDescription: 97-106pSubject(s): Online resources: In: International journal of artificial intelligence and computational researchSummary: State estimation of power systems is essential for continuous monitoring and control of power system. since the power system is nonlinear, extended kalman filter (EKF) is useful for the estimation of dynamic states. one of the major challenges in the design of the EKF is tuning of covariance matrices. Tuning of EKF by trial and error method requires huge computation time and intense decision making. The objective of this paper is to design an extended kalman filter for a synchronous machine connected to an infinnite bus and enhance the filter performance by tuning it using two ibest particle swarm optimization technique. The above designed filter is tested for various operating conditions and the simulation results show the superior performance of the filter in terms of measurement and process noise rejection and accuracy.| Item type | Current library | Status | Barcode | |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2022-1089 |
State estimation of power systems is essential for continuous monitoring and control of power system. since the power system is nonlinear, extended kalman filter (EKF) is useful for the estimation of dynamic states. one of the major challenges in the design of the EKF is tuning of covariance matrices. Tuning of EKF by trial and error method requires huge computation time and intense decision making. The objective of this paper is to design an extended kalman filter for a synchronous machine connected to an infinnite bus and enhance the filter performance by tuning it using two ibest particle swarm optimization technique. The above designed filter is tested for various operating conditions and the simulation results show the superior performance of the filter in terms of measurement and process noise rejection and accuracy.
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