Comparison of adaptive techniques for the prediction of the equivalent salt deposit density of medium voltage insulators

One of the main reasons that flashover can occur in distribution lines is the sea salt contamination of medium voltage insulators. Equivalent salt deposit density (ESDD) expresses the contamination level of insulators and is used as the main criterion for scheduling the maintenance (washing) of insulators. In this paper two different adaptive techniques, a multi model partitioning filter (MMPF) and an artificial neural network (ANN), are developed and presented in order to predict the equivalent salt deposit density of medium voltage insulators. Real data are used for the MMPF modeling and the ANN training, as well as for the comparison of the produced by the two techniques ESDD results with actual measured ones. The proposed techniques can be very useful in the work of electrical maintenance engineers for estimating the insulator’s contamination easily, accurately and at minimum cost resulting in a more effective maintenance scheduling. Key-Words: Artificial neural networks, Equivalent salt deposit density (ESDD), Extended Kalman filters, Medium voltage insulators, Multi model partitioning filter