Analysis of Leakage Current to Predict Insulator Flashover Using Artificial Neural Network

Abstract

Problem statement:The phenomenon of flashover in polluted insulators has been continued by the study of the characteristics of contaminating layers deposited on the surface of insulators in high voltage laboratories. In the literature, Experimental investigations have been carried out on a real insulator or a flat plate model of insulators under high voltage application. This study proposed the Equivalent insulator flat plate model for studying the flashover phenomena due to pollution under wet conditions even at low voltage. Laboratory based tests were carried out on the model under AC voltage at different pollution levels. Different concentrations of salt solution has been prepared using sodium chloride, Kaolin and distilled water representing the various contaminations. Leakage current during the experimental studies were measured for various polluted conditions. Approach: A new model of Vc = f (V, I initial , I em , I emax and I σ ) based on artificial neural network has been developed to predict flashover from the analysis of leakage current. The input variable to the artificial neural network are mean (I mean ), Maximum(Imax) and standard deviation(I σ ) of leakage current extracted along with the initial value of leakage current I initial and the input voltage(V).The target obtained was used to evaluate the performance of the neural network model. Results: The optimum process has been carried out based on the training accuracy measured by RMSE, the network converged to a threshold of 0.0001.The trained model prediction is in good agreement with the actual results and the R 2 value of the developed model is 0.99996. Conclusion: The developed ANN model is well-suited for the analysis of leakage current to predict flashover on the insulator surface with high accuracy.

An alternative approaches to predict flashover voltage on polluted outdoor insulators using artificial intelligence techniques

“…Prior to the test, the insulator was first carefully cleaned using alcohol and water with conductivity lower than 10 µS/cm. Pollution suspension is prepared by mixing an appropriate ratio kaolin with Sodium Chloride (NaCl) in water to achieve the desired conductivity level as described in [10,11]. The salinity of the pollution layer on the insulator is then determined using a conductivity meter.…”

Contribución al mantenimiento de aisladores de alta tensión en servicio basado en medidas de corrientes de fuga

“…Asimismo, se han propuesto las redes neuronales artificiales (ANN) para predecir el nivel de contaminación a partir de las características de la corriente de fuga: valor promedio y máximo, desviación típica [37,[98][99][100], tensión aplicada al aislador entre otras. Físicamente, el valor medio de la corriente de fuga refleja el nivel de contaminación de diferentes cadenas de aisladores en la misma condición de funcionamiento [98].…”