Effective insulator maintenance scheduling using artificial neural networks

One of the most frequent causes of failure of overhead high- and medium-voltage transmission and distribution lines is contamination of the insulators with diverse substances such as saline and industrial substances. The contamination mechanically degrades the insulators and affects the electrical characteristics of the insulating material, leading to flashovers. Periodic maintenance of insulators can reduce or even prevent the outages caused by contamination. The maintenance scheduling is planned based either on measurements, which are quite expensive and time consuming processes or on experience, a definitely inaccurate process. The current work presents a new approach for the assessment of contamination of insulators on the basis of artificial intelligence and, more specifically, artificial neural networks (ANNs). An ANN model is defined and when applied on operating voltage insulators it presented results similar to experimental results. The proposed approach can be useful in the work of electrical maintenance engineers, reducing the time and cost of insulator maintenance.