Particle swarm optimization pattern recognition neural network for transmission lines faults classification

Abstract

The operating conditions of the transmission lines can be assessed through the information measured by the smart meters in the power supply bureau. Accurate classification of transmission line faults can be helpful to improve the maintenance strategy of smart grids. This paper analyzes the mechanism of the voltage loss and the phase fault of the transmission line by using the operation data collected by the smart meters from three power supply bureaus (named Bureau A, B and C), where the faults are labeled by expert systems. In this work, a novel Particle Swarm Optimization Pattern Recognition Neural Network (PSO-PRNN) classifier is built to accurately categorize the faults and its classification performance is compared with the ones of traditional K-Nearest Neighbor (KNN), Decision Tree (DT), PSO-KNN and PSO-DT classifiers. The results show that the classification accuracy of PSO-PRNN outperforms traditional classifiers when being applied to the data collected from all three bureaus. In the A power supply bureau are 83.0%, 88.7%, 82.0%, 86.9% and 96.1%, and the classification accuracy rates are 55.7%, 68.7%, 56.6%, 68.7% and 82.5%, when used to process the data of the bureau B. The classification accuracy is 57.1%, 66.4%, 57.2%, 69.0% and 82.1%, when processing the data of bureau C. The results show that the PSO-PRNN classifier is superior to the others in terms of accuracy and applicability.