Discharge voltage prediction of UHV AC transmission line–tower air gaps by a machine learning model

A MLP Neural Network-Based Unified Intelligent Model for Predicting Short-Gap Breakdown Voltages in Air

“…Finally, AI has the particularity of learning from experience and adapting to situations never seen before, which makes it very suitable for the problem of approximating the electrical insulators' breakdown voltages, particularly air. With this in mind, several ingenious research in the field of breakdown voltage estimation based on intelligent regression approaches have been proposed, including those based on neural networks [11][12][13][14][15], those based on Support Vector Regression (SVR) [16][17][18][19][20], those based on Support Vector Machine (SVM) [11,21,22], those based on fuzzy logic [23,24] and those based on extremely randomized trees algorithm [11]. In what follows, we will detail some studies that are pretty similar to ours:  Yang et al and Yao et al [16,17] proposed an intelligent system based on SVR optimized by Cuckoo Search (CS) and weighted by grey relation analysis (CS-ω-SVR) in order to calculate the 50% breakdown voltage of the long rod-rod gap under different atmospheric conditions and different distances.…”