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The exact location of faults in the electrical distribution systems is a problem that affects not only the users, but also the companies providing the electric service. With greater time invested in this period, the losses due to unbilled energy and inconvenience to users increases, thus decreasing the quality of service. One of the causes of the growth in time is the misunderstanding that might exist in the localization systems that act under the presence of distributed generation sources in the distribution networks. In this sense, the present research develops an intelligent diagnosis of faults in distribution systems with distributed generation. Three stages are defined: Identification of the type of fault, the location of the zone, and the exact point of fault. A mixed method based on artificial intelligence and mathematical algorithms is applied. Eleven different types of faults that can occur in a distribution system are considered with six different values of fault resistances ranging from 5 to 30. The errors found are less than 2% in the location of the fault point with robustness to variations in the load and the penetration of distributed generation.

Hybrid electric aero-propulsion requires high power-density electric motors. The use of a constrained optimization method with the finite element analysis (FEA) is the best way to design these motors and to find the best solutions which maximize the power density. This makes it possible to take into account all the details of the geometry as well as the non-linear characteristics of magnetic materials, the conductive material and the current control strategy. Simulations were performed with a time stepping magnetodynamic solver while taking account the rotor movement and the stator winding was connected by an external electrical circuit. This study describes the magnetic FEA direct optimization approach for the design of Halbach array permanent magnet synchronous motors (PMSMs) and its advantages. An acceptable compromise between precision and computation time to estimate the electromagnetic torque, iron losses and eddy current losses was found. The finite element simulation was paired with analytical models to compute stress on the retaining sleeve, aerodynamic losses, and copper losses. This type of design procedure can be used to find the best machine configurations and establish design rules based on the specifications and materials selected. As an example, optimization results of PM motors minimizing total losses for a 150-kW application are presented for given speeds in the 2000 rpm to 50,000 rpm range. We compare different numbers of poles and power density between 5 kW/kg and 30 kW/kg. The choice of the number of poles is discussed in the function of the motor nominal speed and targeted power density as well as the compromise between iron losses and copper losses. In addition, the interest of having the current-control strategy as an optimization variable to generate a small amount of flux weakening is clearly shown.

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he occurrence of faults in distribution systems has a negative impact on society, and their effects can be reduced by fast and accurate diagnostic systems that allow to identify, locate, and correct the failures. Since the 1990s, fuzzy logic and other artificial intelligence techniques have been implemented to identify faults in distribution systems. The main objective of this paper is to perform fault diagnoses based on fuzzy logic. For conducting the study, the IEEE 34-Node Radial Test Feeder is used. The data was obtained from ATPDraw-based fault simulation on different nodes of the circuit considering three different fault resistance values of 0, 5, and 10 ohms. The fuzzy rules to identify the type of fault are defined using the magnitudes of the phase and neutral currents. All measurements are taken at the substation, and the results show that the proposed technique can perfectly identify and locate the type of failure.