Please use this identifier to cite or link to this item: https://scholar.ptuk.edu.ps/handle/123456789/523
Title: Improved UFLS with Consideration of Power Deficit During Shedding Process And Flexible Load Selection
Authors: Jallad, J.
Keywords: wind turbines
load shedding
hydroelectric generators
particle swarm optimisation
Issue Date: 4-Jan-2018
Publisher: IET Renewable Power Generation
Citation: Jallad, J., Mekhilef, S., Mokhlis, H., & Laghari, J. A. (2018). Improved UFLS with consideration of power deficit during shedding process and flexible load selection. IET Renewable Power Generation, 12(5), 565-575.
Series/Report no.: 12(5);565-575
Abstract: This study presents an improved under-frequency load shedding (UFLS) scheme that can detect power deficit during the shedding process and accordingly adjust the amount of load shedding. This is achieved by continuous monitoring of the overshooting signal of the second frequency derivative of the centre of inertia. Once detected, an equivalent system inertia constant is estimated in order to quantify the new power deficit. The scheme is also equipped with an optimisation algorithm to determine the best combination of loads that is close to the amount of power deficit, which minimises frequency overshoot/undershoot. The optimisation technique selected for this work is based on particle swarm optimisation. The performance of the proposed UFLS scheme was validated using a modified IEEE 33 bus with two mini-hydro generators and one full converter wind turbine. The system and the proposed UFLS was modelled and simulated in PSCAD/EMTDC software. The results confirmed that the proposed scheme is capable of shedding loads with minimum undershoot/overshoot, and detect and estimate a new power deficit during load shedding. The results reported by the proposed scheme proved to be significantly better than those reported by conventional and adaptive load shedding schemes.
URI: https://scholar.ptuk.edu.ps/handle/123456789/523
Appears in Collections:Engineering and Technology Faculty

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