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Energies 2018, 11(8), 2059; https://doi.org/10.3390/en11082059

Parameter Estimation of Electromechanical Oscillation Based on a Constrained EKF with C&I-PSO

1
College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
2
ABB Inc., Raleigh, NC 27606, USA
3
Melentiev Energy Systems Institute, Russian Academy of Sciences, Irkutsk 664033, Russia
*
Author to whom correspondence should be addressed.
Received: 18 July 2018 / Revised: 3 August 2018 / Accepted: 6 August 2018 / Published: 8 August 2018
(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)
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Abstract

By combining together the extended Kalman filter with a newly developed C&I particle swarm optimization algorithm (C&I-PSO), a novel estimation method is proposed for parameter estimation of electromechanical oscillation, in which critical physical constraints on the parameters are taken into account. Based on the extended Kalman filtering algorithm, the constrained parameter estimation problem is formulated via the projection method. Then, by utilizing the penalty function method, the obtained constrained optimization problem could be converted into an equivalent unconstrained optimization problem; finally, the C&I-PSO algorithm is developed to address the unconstrained optimization problem. Therefore, the parameters of electromechanical oscillation with physical constraints can be successfully estimated and better performed. Finally, the effectiveness of the obtained results has been illustrated by several test systems. View Full-Text
Keywords: constrained parameter estimation; extended Kalman filter; power systems; C&I particle swarm optimization; ringdown detection constrained parameter estimation; extended Kalman filter; power systems; C&I particle swarm optimization; ringdown detection
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Sun, Y.; Wang, Y.; Bai, L.; Hu, Y.; Sidorov, D.; Panasetsky, D. Parameter Estimation of Electromechanical Oscillation Based on a Constrained EKF with C&I-PSO. Energies 2018, 11, 2059.

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