Analyzing the Effects of Abnormal Resonance Voltages using Artificial Neural Networks

Abstract

The possibilities of using artificial neural networks (ANNs) for quick decision-making in the events of prolonged surges are presented in this paper considering that neural networks can establish non-linear relationships between the parameters of an ultra-high voltage transmission line. Research has been carried out based on theoretical models as well as practical problems aiming at the analysis of resonant overvoltages during their occurrence, development and existence. Determining of overvoltage characteristics was carried out in the presence of a significant number of fuzzy specified factors affecting the accuracy. The multilayer model, suitable for identifying the factors having the greatest impact on the occurrence, frequency and multiplicity of overvoltages in electrical networks, is applied. The resonant overvoltages were generated by connecting the autotransformer to the electrical bulk network. The results of determining the characteristics of resonant overvoltages using ANNs are presented in this paper. To achieve this goal, the following four tasks were formulated: (i) overvoltage characteristics using neural network methods were determined, (ii) neural network model corresponding to power line initial data was built, (iii) forecasted results were obtained, and (iv) the accuracy of constructed model was evaluated.