The planning problem of electrical power distribution networks, stated as a mixed nonlinear integer optimization problem, is solved using the ant colony system algorithm (ACS). The behavior of real ants has inspired the development of the ACS algorithm, an improved version of the ant system (AS) algorithm, which reproduces the technique used by ants to construct their food recollection routes from their nest, and where a set of artificial ants cooperate to find the best solution through the interchange of the information contained in the pheromone deposits of the different trajectories. This metaheuristic approach has proven to be very robust when applied to global optimization problems of a combinatorial nature, such as the traveling salesman and the quadratic assignment problem, and is favorably compared to other solution approaches such as genetic algorithms (GAs) and simulated annealing techniques. In this work, the ACS methodology is coupled with a conventional distribution system load-flow algorithm and adapted to solve the primary distribution system planning problem. The application of the proposed methodology to two real cases is presented: a 34.5-kV system with 23 nodes from the oil industry and a more complex 10-kV electrical distribution system with 201 nodes that feeds an urban area. The performance of the proposed approach outstands positively when compared to GAs, obtaining improved results with significant reductions in the solution time. The technique is shown as a flexible and powerful tool for the distribution system planning engineers.

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Is there a limit to the maximum resolution one can achieve when representing the signal's energy in the TimeFrequency plane? Some authors sustain that such a limit exists, and ignoring it is the cause of the known difficulties with some joint Time-Frequency distributions; others maintain that there is no such limit.In this article, we propose to analyze the merits and demerits of the several existing approaches, and suggest further arguments one might wish to consider. This will take us to the conclusion that, both from a tool-specific and from a general information-theoretic point of view, there is, indeed, a lower limit on the achievable resolution, even though the expression for that limit can not be given by the traditional Heisenberg-Gabor relations.

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