Radial basis function neural network for power system load-flow

A. Karami*, M. S. Mohammadi

*Corresponding author for this work

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network.

Original languageEnglish
Pages (from-to)60-66
Number of pages7
JournalInternational Journal of Electrical Power and Energy Systems
Volume30
Issue number1
DOIs
Publication statusPublished - Jan 2008
Externally publishedYes

Keywords

  • Multi-layer perceptron neural network
  • Power systems load-flow
  • Radial basis function neural network

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