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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.

Suffix arrays form a powerful data structure for pattern detection and matching. In a previous work, we presented a novel algorithm (COV) which is the only algorithm that allows the detection of all repeated patterns in a time series by using the actual suffix array. However, the requirements for storing the actual suffix strings even on external media makes the use of suffix arrays impossible for very large time series. We have already proved that using the concept of Longest Expected Repeated Pattern (LERP) allows the actual suffices to be stored in linear capacity O(n) on external media. The repeated pattern detection using LERP has analogous time complexity, and thus makes the analysis of large time series feasible and limited only to the size Further, when empirical knowledge related to the distribution of repeated pattern's length is available, the proposed method (MLERP) can achieve better time performance compared to the standard LERP method and definitely much better than using any other pattern matching algorithm and applying brute force techniques which are unfeasible in logical (human) time frame. Thus, we may argue that MLERP is a very useful tool for detecting all repeated patterns in a time series regardless of its size and hardware limitations.

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