No abstract

The power distribution system is the most critical, among the power systems, in delivering electricity. Consequently, faults that occur in most cases due to the weather, can cause diverse socio-economic impacts. Hence, considering fault location, the lion's share of the literature addresses maintenance crews patrol routing by merely regarding the power distribution system faults, despite possibly blocked roads or devices accessibility be affected by the weather as well. To properly optimize power distribution system crews inspection routing, the blocked roads must be avoided and the optimization must be conducted to reachable devices. This process is initiated by ltering the blocked roads from the road-network, then a genetic algorithm based on permutation operators is employed on the suitably coded solutions. Furthermore, it was proposed a test case, for the optimization procedure, with a road-network, where the blockedroads were gradually included, and a power distribution system. The resulting solutions showed optimized inspection routes that deviate from blocked roads and skipped from unreachable devices, which is a possible consequence of road-network disruptions. In this manner, they may impact on power distribution maintenance crews routing. Therefore, the suggested methodology proved suitable for a maintenance crew routing under road-network blockage.

Electricity is a fundamental resource for modern society. However, some threats are faced by Electrical Power Distribution Systems, which are responsible for delivering electricity to end consumers. Analyzing how much time these hazards will threaten these systems, causing failure events, is an essential area of study. Through statistical methods, it is possible to study this behaviour from time until failure, as well as to observe the influence of variables at this time,providing models to predict when a failure event will occur. In this study, Reliability Analysis Regression techniques are used on real data, constructing a model for all failures and for different groups of failures, using non-parametric and parametric methods to estimate the reliability and cumulative hazard curves. An analysis of the failure causes directly linked to weather events, using six weather variables, is also made.