Radial Basis Function Neural Network Application to Power System Restoration Studies

Radial Basis Function Neural Network Application to Power System Restoration Studies

One of the most important issues in power system restoration is overvoltages caused by transformer switching. These overvoltages might damage some equipment and delay power system restoration. This paper presents a radial basis function neural network (RBFNN) to study transformer switching overvoltages. To achieve good generalization capability for developed RBFNN, equivalent parameters of the ...

Fast Voltage and Power Flow Contingency Ranking Using Enhanced Radial Basis Function Neural Network

Deregulation of power system in recent years has changed static security assessment to the major concerns for which fast and accurate evaluation methodology is needed. Contingencies related to voltage violations and power line overloading have been responsible for power system collapse. This paper presents an enhanced radial basis function neural network (RBFNN) approach for on-line ranking of ...

Training Radial Basis Function Neural Network using Stochastic Fractal Search Algorithm to Classify Sonar Dataset

Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorith...

Application of Radial Basis Function Neural Network in Environmental Chemistry

The retention behaviour of 66 organic pollutants in biocompatible micelles was studied by QSPR method. The linear and nonlinear models between the structures of these compounds and their chromatographic retention values were established by using the heuristic method and the RBFNN method, respectively. The correlation coefficients of the two methods are 0.8400 and 0.8642, respectively, and the c...

A supervised radial basis function neural network