neural network design for energy estimation in surge arresters

In power systems, the transmission and distribution networks of electrical energy rely heavily on the performance of various equipment. any malfunction within these systems can lead to network interruptions, short circuits, and power failures. arresters are critical devices used to limit transient overvoltages caused by lightning strikes and switching events in transmission and distribution networks. these arresters protect equipment from transient overvoltages while ensuring that they do not react to temporary overloads. their effectiveness is influenced by environmental conditions, such as humidity and pollution. this research aims to analyze the factors affecting voltage and energy absorption during lightning strikes on power systems. additionally, we focus on designing an artificial neural network (ann) to estimate the energy absorbed by the arrester, minimizing the error of this neural network. the results demonstrate that the ann can effectively estimate the power of the arrester within the power system, providing a valuable tool for enhancing system reliability and performance. this study contributes to the understanding of arrester behavior under transient conditions and offers a novel approach to estimating their energy absorption capabilities using advanced computational techniques.