PREDICTION OF FLASHOVER VOLTAGE OF INSULATORS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Abstract

High voltage insulators form an essential part of the high voltage electric power transmission systems. Any failure in the satisfactory performance of high voltage insulators will result in considerable loss of capital, as there are numerous industries that depend upon the availability of an uninterrupted power supply. The importance of the research on insulator pollution has been increased considerably with the rise of the voltage of transmission lines. In order to determine the flashover behavior of polluted high voltage insulators and to identify to physical mechanisms that govern this phenomenon, the researchers have been brought to establish a modeling. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model to estimate the critical flashover voltage (FOV) for polluted insulators, using experimental measurements carried out in an insulator test station according to the IEC norm and a mathematical model based on the characteristics of the insulator: the diameter, the height, the creepage distance, the form factor and the equivalent salt deposit density and estimates the critical flashover voltage. In order to train the network and to test its performance, the data sets are derived from experimental results obtained from the literature and a mathematical model. The obtained results are promising and insure that ANFIS techniques can estimate the critical flashover voltage for new designed insulators with different operating conditions and