Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/702
Title: Neural networks and static voltage stability in power systems
Authors: Khaldi, Mohamad 
Affiliations: Department of Electrical Engineering 
Keywords: Static VAr compensators
Fault diagnosis
Power system analysis computing
Power system stability
Power transformers
Subjects: Artificial intelligence
Issue Date: 2008
Publisher: IEEE
Part of: IEEE International Conference on Industrial Technology
Start page: 1
End page: 6
Conference: IEEE International Conference on Industrial Technology (21-24 April 2008 : Chengdu, China) 
Abstract: 
A power system is an interconnected network of mainly generation, transmission/distribution, and consumption of electrical energy. The steady-state operation of maintaining voltage stability is done by switching various controllers scattered all over the network. Some of the common controllers are the PV or generation buses voltage magnitudes, static reactive power (VAR) compensators, and under load tap transformers. When a contingency occurs, whether forced or unforced, the dispatcher in the Energy Control Center is to alleviate the problem in a minimum time, cost, and effort. Persistent problem may lead to blackout. The dispatcher is to have the appropriate switching of controllers in terms of type, location, and size to remove the contingency and maintain voltage stability. Wrong switching may worsen the problem and that may lead to blackout. Thus, to do this intricate and delicate switching is a challenging task and requires highly experienced dispatcher. This work proposed and used an Artificial Neural Network (ANN) to assist the dispatcher in the decision making.
URI: https://scholarhub.balamand.edu.lb/handle/uob/702
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Electrical Engineering

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