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This paper evaluates the behavior related to the main variables that influence in the quantification of potential points for installation of fault indicators along electric power distribution feeders. Moreover, based on these behavioral characteristics, fuzzy inference systems are also used to estimate the best positions to allocate fault indicators, which take into account the distance in that a particular bus is in relation to more adj acent protection devices, load profile and short-circuit current levels of the downstream system to the respective points. Results with real data highlight the efficiency of the proposed methodology.Index Terms--Artificial intelligence, fault indicator, fuzzy inference system, power system protection.

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The paper describes a novel neural model to estimate electrical losses in transformer during the manufacturing phase. The network acts as an identifier of structural features on electrical loss process, so that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through experimental data taking into account core losses, copper losses, resistance, current and temperature. The results obtained in the simulations have shown that the developed technique can be used as an alternative tool to make the analysis of electrical losses on distribution transformer more appropriate regarding to manufacturing process. Thus, this research has led to an improvement on the rational use of energy.

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.