Feed-Forward Neural Networks Training with Hybrid Taguchi Vortex Search Algorithm for Transmission Line Fault Classification

Abstract

In this study, the hybrid Taguchi vortex search (HTVS) algorithm, which exhibits a rapid convergence rate and avoids local optima, is employed as a new training algorithm for feed-forward neural networks (FNNs) and its performance was analyzed by comparing it with the vortex search (VS) algorithm, the particle swarm optimization (PSO) algorithm, the gravitational search algorithm (GSA) and the hybrid PSOGSA algorithm. The HTVS-based FNN (FNNHTVS) algorithm was applied to three datasets (iris classification, wine recognition and seed classification) taken from the UCI database (the machine learning repository of the University of California at Irvine) and to the 3-bit parity problem. The obtained statistical results were recorded for comparison. Then, the proposed algorithm was used for fault classification on transmission lines. A dataset was created using 735 kV, 60 Hz, 100 km transmission lines for different fault types, fault locations, fault resistance values and fault inception angles. The FNNHTVS algorithm was applied to this dataset and its performance was tested in comparison with that of other classifiers. The results indicated that the performance of the FNNHTVS algorithm was at least as successful as that of the other comparison algorithms. It has been shown that the FNN model trained with HTVS can be used as a capable alternative algorithm for the solution of classification problems.

Keywords

• fault classification
• HTVS algorithm
• optimization
• training feed-forward neural networks