Particle Swarm Optimization trained recurrent neural network for voltage instability prediction

Particle Swarm Optimization trained recurrent neural network for voltage instability prediction

Voltage instability is considered as a major problem that faces the power systems during its operation. Voltage instability prediction is necessary for avoiding voltage collapse. This paper investigates the performance of recurrent neural network (RNN) in voltage instability prediction. A recurrent...

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Bibliographic Details
Main Authors: Amr M. Ibrahim, Noha H. El-Amary
Format: Article
Language:English
Published: SpringerOpen 2018-09-01
Series:Journal of Electrical Systems and Information Technology
Online Access:http://www.sciencedirect.com/science/article/pii/S231471721730020X
Description
Summary:Voltage instability is considered as a major problem that faces the power systems during its operation. Voltage instability prediction is necessary for avoiding voltage collapse. This paper investigates the performance of recurrent neural network (RNN) in voltage instability prediction. A recurrent neural network trained with Particle Swarm Optimization (PSO) is proposed in this paper. The proposed method is examined on 14-bus and 30-bus IEEE standard systems. These systems are simulated using MATLAB/Power System Toolbox program. Also, a detailed comparison between PSO algorithm and Backpropagation (BP) algorithm is discussed. The results proved the effectiveness of the proposed method. Keywords: Backpropagation algorithm, Particle Swarm Optimization technique, Recurrent neural network, Voltage instability predictor, Voltage stability
ISSN:2314-7172

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