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The Brazilian electrical utility companies must meet continuity indicators for energy supply, which are represented by the indices of average interruption duration and frequency, according to targets established by the Brazilian Regulatory Agency for Electrical Energy (ANEEL). In a nationwide base, ANEEL has defined 30 clusters, each one having specific targets for Customer Average Duration Interruption Index and Customer Average Frequency Interruption Index; still, very frequently the utility distribution companies are financially penalised for not meeting these indicator targets. This study proposes a decision support system based on machine learning techniques so that the utility distribution companies can emulate the characteristics and procedures used by the ANEEL, and help in obtaining more adequate customer groups to evaluate the duration and frequency indicators. The proposed system was applied in a case study of a distribution utility whose supply area is located in the Brazilian Amazonia. The methodology proved to be adequate for seeking better customer grouping configurations that could result in a decrease in goal violations as well as providing more consistent goals, considering the specific characteristics of each distribution utility. Although this methodology was applied to a Brazilian scenario it also can be applied to other distribution utilities worldwide.

Conventional outage management practices in distribution systems are tedious and complex due to the long time taken to locate the fault. Emerging smart technologies and various cloud services offered could be utilized and integrated into the power industry to enhance the overall process, especially in the fault monitoring and normalizing fields in distribution systems. This paper introduces smart fault monitoring and normalizing technologies in distribution systems by using one of the most popular cloud service platforms, the Microsoft Azure Internet of Things (IoT) Hub, together with some of the related services. A hardware prototype was constructed based on part of a real underground distribution system network, and the fault monitoring and normalizing techniques were integrated to form a system. Such a system with IoT integration effectively reduces the power outage experienced by customers in the healthy section of the faulted feeder from approximately 1 h to less than 5 min and is able to improve the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) in electric utility companies significantly.