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Estimating the voltage collapse proximity indicator using artificial neural network
Faculty
Engineering
Year:
2001
Type of Publication:
Article
Pages:
69-79
Authors:
SALAMA, MM, Ghariany, EF, Saied, EM, Abou-Elsaad, MM
DOI:
10.1016/S0196-8904(00)00023-6
Journal:
ENERGY CONVERSION AND MANAGEMENT PERGAMON-ELSEVIER SCIENCE LTD
Volume:
42
Research Area:
Thermodynamics; Energy \& Fuels; Mechanics; Physics
ISSN
ISI:000089296600006
Keywords :
power systems, voltage security, voltage instability, voltage collapse, neural network
Abstract:
Modern pourer systems are currently operating under heavily loaded conditions due to various economic, environmental and regulatory changes. Consequently, maintaining voltage stability has become a growing concern for electric power utilities, With increased loading and exploitation of the power transmission system, the problem of voltage stability and voltage collapse attracts more and more attention, A voltage collapse can take place in systems and subsystems and can appear quite abruptly, There are different methods used to study the voltage collapse phenomenon, such as the Jacobian method, the voltage instability proximity index and the voltage collapse proximity indicator method. This paper is concerned with the problem of voltage stability and investigates a proposed voltage collapse proximity indicator applicable to the load points of a power system. Voltage instability is early predicted using artificial neural networks (ANN) on the basis of a voltage collapse proximity indicator. Different system loading strategies are studied and evaluated. Test results on a sample power system demonstrate the merits of the proposed approach. The objective of this paper is to present the application of ANN in estimating the voltage collapse proximity indicator of a power system. (C) 2000 Elsevier Science Ltd. All rights reserved.
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