Probabilistic Neural Network Classifier for Static Voltage Security Assessment of Power Systems

In this article, a probabilistic neural network based classifier is used to evaluate the static voltage security of the power system under two contingency cases. In the first case, the neural network classifies the system security based on the voltage profile of each bus in reference to changes in t...

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Bibliographic Details
Published in:Electric power components and systems 2012-01, Vol.40 (2), p.147-160
Main Authors: Abdelaziz, A. Y., Mekhamer, S. F., Badr, M. A. L., Khattab, H. M.
Format: Article
Language:English
Subjects:
Comparative analysis
Contingency
Electric currents
Electric potential
Electric power generation
Electricity generation
IEEE standards
line outage
Neural networks
power system classifier
Probabilistic methods
probabilistic neural network
Probability theory
radial basis function neural network
Security
static security
Voltage
voltage level
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Summary:In this article, a probabilistic neural network based classifier is used to evaluate the static voltage security of the power system under two contingency cases. In the first case, the neural network classifies the system security based on the voltage profile of each bus in reference to changes in the generation and load profile in the system contingencies, while in the second case, single and double line outages of the system are used to examine the system security. The performance of the proposed probabilistic neural network is compared with the radial basis function neural network and the back-propagation neural network. The probabilistic neural network achieves superior results in comparison to other types of neural networks in both test contingencies. The proposed methodology is examined using IEEE standard test systems, where the output of the probabilistic neural network in each test contingency classifies the security of the power system into three classes-normal, alert, and emergency.
ISSN:1532-5008
1532-5016
DOI:10.1080/15325008.2011.629332