Fault detection and classification are crucial procedures for electric power distribution systems because they can minimize the occurrence of faults. The methods for fault detection and classification have become more problematic because of the significant expansion of distributed energy resources in distribution systems and the change in their currents due to the action of short-circuiting. In this context, to fill this gap, this study presents a robust methodology for short-circuit fault detection and classification with the insertion of distributed generation units. The proposal methodology progresses in two stages: in the former stage, the detection is based on the continuous analysis of three-phase currents, whose characteristics are extracted through maximal overlap discrete wavelet transform. In the latter stage, the classification is based on three fuzzy inference systems to identify the phases with disturbance. The short-circuit type is identified by counting the shorted phases. The algorithm for short-circuit fault detection and classification is developed in MATLAB programming environment. The methodology is implemented in a modified IEEE 34-bus test system and modeled in ATPDraw with three scenarios with and without distributed generation units and considering the following parameters: fault type (single-phase, two-phase, and three-phase), angle of incidence, fault resistance (high impedance fault and low impedance fault), fault location bus, and distributed generation units (synchronous generators and photovoltaic panels). The accuracy is greater than 94.9% for the detection and classification of short-circuit faults for more than 20,000 simulated cases.

The aim of the present study was to evaluate the serological methods using ELISA with recombinant-rK39 (ELISA-rK-39) and soluble extract-SE (ELISA-SE) antigens, the indirect fluorescence antibody test (IFAT) in comparison to an immunochromatography rapid diagnostic test (RDT-rK39) and with a direct parasitological exam (PA) for Canine Visceral Leishmaniasis (CVL) diagnosis. The results showed that 89% (60/67) of the dogs were positive for at least one serological diagnostic test. ELISA-SE was the test that detected anti-Leishmania antibodies in the serum of the highest number of dogs (71.6%) followed by ELISA-rK39 (65.7%), IFAT (65.7%) and RDT-rK39 (55.2%). PA detected the lowest numbers (40.3%) of positive dogs. In relation to the total of examined dogs, the Kappa indexes (p ≤ 0.05) showed a good agreement between ELISA-SE and IFAT (88.1%; k = 0.7237), and it was also observed in the comparison of RDT-rK39 with ELISA-SE (83.6%, k= 0.6561), IFAT (83.5%, k= 0.6605) and PA (85.0%, k= 0.7074). A bad agreement was detected in any association of ELISA-rk39 with the other tests in either symptomatic or asymptomatic animals. ELISA as well as RDT using recombinant antigenic protein (rK39) were the methods that detected the lowest prevalence rates (33.3%) of CVL in asymptomatic dogs. In conclusion, only one test does not adequately identify dogs with CVL and it is necessary the association of two or more diagnostic tests. Because of the good agreement indexes of RDT-rK39 when evaluated with ELISA-SE, IFAT and PA it was suggested as a complementary method to be used in association with either ELISA-SE or IFAT, particularly in the symptomatic dogs. Furthermore, new studies are recommended in order to improve the sensitivity of tests mainly for asymptomatic dogs.