A Hybrid PSO-ANN-based Fault Classification System for EHV Transmission Lines

Abstract

The paper presents a robust design of Artificial Neural Network Classifier used for the classification of faults occurring on Extra High Voltage Transmission lines with multiple series compensation. The feature selection and model parameters play a vital role in providing better accuracy as well as stability of Classifier in recognition of fault patterns. The paper describes the use of Particle Swarm Optimization (PSO)-assisted training of Artificial Neural Network (ANN). The PSO is used to find the optimal number of features to attain sufficiently high accuracy along with optimal design parameters of ANN so as to reduce the computational burden. A combined use of Multiresolution Wavelet Analysis (MRA) and Statistical Features (SF) module is shown for fault pattern analysis. A Particle Swarm optimization based Neural Network with Feature Selection (PSONNFS) Algorithm developed is applied on a large database of fault patterns with a wide range of operating conditions in series compensated system. The best features found from the PSONNFS algorithm are later used to find the optimal structure of ANN. The results obtained provide an enhanced model of ANN with a high degree of accuracy with optimality in Classifier parameters. A Genetic Algorithm based Neural Network optimization with Feature Selection (GANNFS) is also developed to verify the performance of the classifier. The results of the PSONNFS algorithm are further compared with the GANNFS algorithm. The results obtained show an enhanced model of ANN using PSONNFS which has a high degree of accuracy with optimality in Classifier parameters.