The distribution network reconfiguration problem (DNRP) refers to the challenge of searching for a given power distribution network configuration with better operating conditions, such as minimized energy losses and improved voltage levels. To accomplish that, this paper revisits the branch exchange heuristics and presents a method in which it is coupled with other techniques such as evolutionary metaheuristics and cluster analysis. The methodology is applied to four benchmark networks, the 33-, 70-, 84-, and 136-bus networks, and the results are compared with those available in the literature, using the criteria of the number of power flow executions. The methodology minimized the four systems starting from the initial configuration of the network. The main contributions of this work are the use of clustering techniques to reduce the search space of the DNRP; the consideration of voltage regulation banks and voltage-dependent loads in the feeder, requiring the addition of a constraint to the mono-objective model to guarantee the transferred load will be supplied at the best voltage magnitude level, and the application of the methodology in real distribution networks to solve a set of 81 real DNRPs from CEMIG-D (the distribution branch of the Energy Company of Minas Gerais). Four out of those are presented as case studies to demonstrate the applicability of the approach, which efficiently found configurations with lower power and energy losses with few PF runs.

This paper introduces an alarm processing without using any connectivity information of the substation electrical network. In order to do so, timestamp and location readily defines specific alarm patterns. In this sense, the proposed algorithm resembles the typical procedure that operators do. This specialized alarm processing allows fast recognition of the most important events in a substation: typically, more than 70% of the alarms belong to the three groups presented in this paper. Tests in a notebook show that it is possible to process 1025 alarms per second with a state machine that model the specialist behaviour of each group, while a typical maximum density is 1,000 alarms per minute.