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  5. Probabilistic Under Frequency Load Shedding Considering RoCoF Relays of Distributed Generators
 
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Probabilistic Under Frequency Load Shedding Considering RoCoF Relays of Distributed Generators

Author(s)
Amraee, Turaj  
Darebaghi, Mohammad Ghaderi  
Soroudi, Alireza  
Keane, Andrew  
Uri
http://hdl.handle.net/10197/9814
Date Issued
2017-12-29
Date Available
2019-04-04T09:45:38Z
Abstract
The activation of Under Frequency Load Shedding (UFLS) is the last automated action against the severe frequency drops in order to re-balance the system. In this paper, the setting parameters of a multistage load shedding plan are obtained and optimized using a discretized model of dynamic system frequency response. The uncertainties of system parameters including inertia time constant, load damping and generation deficiency are taken into account. The proposed UFLS model is formulated as a mixed integer linear programming optimization problem to minimize the expected amount of load shedding. The activation of Rate-of-Change-of-Frequency (RoCoF) relays as the anti-islanding protection of Distributed Generators (DGs) are considered. The MCS method is utilized for modeling the uncertainties of system parameters. The results of probabilistic UFLS are then utilized to design four different UFLS strategies. The proposed dynamic UFLS plans are simulated over the IEEE 39-bus and the large-scale practical Iranian national grid.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
IEEE
Journal
IEEE Transactions on Power Systems
Volume
33
Issue
4
Start Page
3587
End Page
3598
Copyright (Published Version)
2017 IEEE
Subjects

Under frequency load ...

Inertia time constant...

Load damping

RoCoF

Uncertainty

DOI
10.1109/TPWRS.2017.2787861
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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TPWRS_01720_2016_R3_(1).pdf

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Owning collection
Electrical and Electronic Engineering Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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