Neural Network Model to Estimate Resistivity of Ground Enhancers Reinforced with Graphene Nano Particles for Transmission Lines

Abstract

For many high voltage transmission lines, lightning is the first cause of outages. Different alternatives have been used to diminish these outages, like the use of counterpoise wires, installation of surge arresters, and the improvement of the grounding system using ground enhancers or chemical enhancers. In this paper, graphene nanoparticles were used to reformulate commercial ground enhancers. The results of this research end up in an improvement factor of up to 100 times the reduction in resistivity, when graphene nanoparticles are used. After lightning current impulse tests done on both types of samples, the performance of the un-reformulated ground enhancer samples shows a faster deterioration than the graphene reinforced ground enhancer samples. In order to establish a criterion to quantitatively rank the chemical ground enhancers ́ performance after the lightning impulse current tests, a neural network model was developed.