In this paper a probabilistic model is presented to optimize the expansion of distributed generation in the electricity distribution network. The Monte Carlo technique is used to obtain probability distributions of the desired variables, such as: power flows, output power of distributed generators, costs, etc. The analysis of the results leads to optimized criteria for the expansion of distributed generation (DG) in distribution networks.

A high penetration of distributed generation in electricity networks makes it necessary to adapt the network to the new conditions of generation and consumption. Storage units can be converted within a few years in another element of power grids. Therefore, it is necessary to analyze the network to determine the optimal location of distributed generation, which lines need to be built or where to install the storage units. This paper presents a model of power network planning which takes into account the effect of the expansion of distributed generation. The results obtained show the continuing replacement of conventional generation by distributed generation and the importance of storage units in this process of replacement.

In this article, particle image velocimetry studies were conducted in a low-speed wind tunnel to investigate the effects of blowing ratio and blade span in terms of the characteristics of the flow field around a film-cooled blade leading edge. The measurements were performed at 20%, 40%, 60%, and 80% of blade span and blowing ratios of M = 0.5, M = 0.75, M = 1, M = 1.5, and M = 2. Velocity, turbulence intensity, and structure of vortices during the interaction between cooling flow and mainstream were analyzed in detail. The analysis shows a significant increase in mainstream velocity at low blowing ratios, M \ 1. Peaks of turbulence were observed at low-and high-span locations. Aerodynamical losses are expected at higher blowing ratios due to the formation of secondary vortices near the outgoing jet. These vortices were a consequence of velocity gradients at this zone.

This article presents results for the design of a fuel injection diagnostic strategy for motor vehicles in Ciudad Juárez, Chihuahua. The first part of this research develops a diagnosis model for the injector MD162524, implementing Fuzzy Logic. Characterization of the diagnostic model took five injector operation tests (spray pattern, injector opening cycles at 3 revolution levels, electrical resistance measurements in the injector, injector flow at a constant pressure and activation of two levels of pressure in the injector) and 3 possible outcomes of diagnostic status (good shape, repairable, useless). In the final part of the investigation, two evolutionary algorithms are used as a tool to optimize the response of the model designed, generating data sets with decreasing measurement error.