NDLI: Coordinated tuning of a set of Static Var Compensators using Evolutionary Algorithms

Content Provider	IEEE Xplore Digital Library
Author	Chaparro, E.R. Sosa, M.L.
Copyright Year	2011
Description	Author affiliation: Polytechnic of National University of Ciudad del Este (FP.UNE), Research Department, Paraguay (Sosa, M.L.) \|\| ITAIPU Binacional, Operation and Dispatch Department, Ciudad del Este - Paraguay (Chaparro, E.R.)
Abstract	The coordinated tuning of a group of Static Var Compensators (SVC), considering several operating conditions at steady state, allows the Electric Power Systems (EPS) to operate close to their overload limits, maintaining an optimal voltage level. The coordinated adjusting problem was considered as an optimization problem with three objective functions to minimize: the financial investment for acquiring the set of compensators, the maximum voltage deviation and the total active power loss. The Genetic Algorithm (GA) was utilized and adapted for multi-objective optimization, obtaining a Multi-Objective GA (MOGA). The parameters to be adjusted in each compensator are: the reference voltage, the maximum and minimum reactive power to be injected in the system. The number of compensators, and their locations, were calculated by the Q-V sensitivity curve. The proposed coordinated tuning method will be validated with a high dimension EPS. Time simulations were made for dynamic performing considering single contingencies.
Starting Page	1
Ending Page	6
File Size	730513
Page Count	6
File Format	PDF
ISBN	9781424484195
e-ISBN	9781424484188
e-ISBN	9781424484171
DOI	10.1109/PTC.2011.6019284
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-19
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Static VAr compensators Tuning Mathematical model Reactive power Voltage control Equations Optimization Genetic Algorithm Static Var Compensator Coordinated tuning Multi-Objective Optimization
Content Type	Text
Resource Type	Article

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