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The benefits of implementing Demand Response (DR) programs in large consumers have been widely documented. However, it is still necessary to explore in more detail the impacts of the implementation of these programs in residential users on the benefits of the distribution system operators. In this paper, we determine the effects of Demand Response (DR) on the reconfiguration of a distribution system considering the consumption characteristics of residential users in Colombia. For this purpose, the demand curve of a typical residential user was constructed for Bogotá D.C., and measurements were taken to estimate the participation of this user in a DR program. In addition, an algorithm based on Particle Swarm Optimization is used as a solution strategy for the reconfiguration problem. The results show that a distribution system reconfiguration could significantly increase the revenues obtained by a Network Operator. However, it is shown that the reduction of energy losses obtained from the DR would not provide sufficient incentives for them to promote the participation of residential users in this kind of programs. [ABSTRACT FROM AUTHOR]

Mathematical Model of a Series-Parallel (SP) photovoltaic (PV) generator represents each module through an equivalent electrical circuit denominated single diode model, this model has associated a nonlinear equation system that describes the electrical behavior of SP generator. This paper presents a solution of this system using optimization methods widely using: Trust Region Dogleg and Particle swarm optimization (PSO) for solving the electrical model of PV generator operating under homogeneous or non-homogeneous conditions, changing the number of submodules and shading pattern. It has been made simulations about generators composed by 3 and 15 series submodules, operating under different partial shading conditions. Between the implemented methods, Trust Region Dogleg show a better performance than other methods, with 2 and 14 times less computation time than the reference method and PSO, respectively, and a RMSE equal or 50% lower than PSO. [ABSTRACT FROM AUTHOR]

In this paper the particle swarm optimization (PSO) along with some of its variants is explored to give solution to the optimal reactive power dispatch problem (ORPD). This problem consists of the optimal use of reactive power resources in a power system, usually with the aim of minimizing power loses and improving voltage profile. The ORPD is a non-lineal and non-convex problem that involves continues and discrete variables and features multiple sub-optimal solutions. Besides the PSO technique, the variants known as Turbulent PSO (TPSO) and Turbulent Crazy (PSO) were implemented. Several tests were performed on IEEE 57 and 118 bus test systems comparing results with other reported in the technical literature. It was found that the performance of PSO is considerably improved when the variants TPSO and TCPSO are applied. [ABSTRACT FROM AUTHOR]

This paper analyzes the effect of different configurations of initial conditions for the optimization algorithm based on particle swarms with vorticity behavior. The proposed algorithm combines the gradient-based search and particle swarm behavior, thus, this algorithm can be affected by the initial conditions for the particles. Finally, a 2D test function is used to observe the characteristics of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the application of a methodology for parameter estimation of a measurement-based exponential recovery load model is described. An optimization problem is proposed for parameter estimation of the load model, and particle swarm optimization is used for its solution. The proposed methodology was tested on a power system, where load was represented by a composite load, which is affected by bus voltage variations. Based on the results, a suitable load model for the analyzed disturbances is obtained using the proposed methodology. [ABSTRACT FROM AUTHOR]

Classic and fuzzy Scan methods are widely used for cluster detection over a lineal or circular data sequence. This sequence has been previously processed and transformed into a binary sequence. The value number one represents the category that is of interest and the zero value represents the rest. The objective of these methods is to detect a cluster of ones. These methods depend on some parameters: the width of the mobile window that covers the entrance sequence, the step of the window's movement and the size of the fuzzy part in the Fuzzy Scan methods. In this paper it is shown how with the combination of a bioinspired algorithm (particle swarm optimization) is useful to find the adequate values of the parameters of the Scan techniques. Also, a Bioinformatics problem is solved as an example. [ABSTRACT FROM AUTHOR]

This research paper shows the simulation results of the thermal design of a micro channel heath sink. A minimum entropy generation criterion was used to generate the mathematical model, which was then solved by Lagrange multipliers and by particle swarm optimization (PSO). It was found that due to the extensive number of variables, traditional techniques demand elevated computational resources since it requires solving a system of non - linear equations. This system was solved using the multidimensional Newton - Raphson method, which needs a set of proposed initial solutions, without having technical criteria for choosing it. On the other hand, particle swarm optimization provides a rather simple solution to the problem. Results achieved with both methods are compared. [ABSTRACT FROM AUTHOR]

This paper shows the comparison made between the particle swarm optimization (PSO) algorithm and the interval analysis optimization method for solving nonlinear-function optimization with equality and/or inequality constraints. The interval analysis optimization method (IAO) was based on the one initially proposed by Ichida (1996). It was used to find the global optimum of a multimodal function with up to three variables, which is subject to equality and inequality constraints. It was found that the PSO algorithm was significantly faster for all functions, although its precision was limited. On the other hand, the IAO method was accurate in all cases, but took a considerably longer computational time. [ABSTRACT FROM AUTHOR]

This paper presents a structural damage detection system. Variations in elasticity module are used as a damage condition in a structural finite element model. This deviation alters vibration natural frequencies and shape modes; which is the fault indicator in this proposal. Noise and incomplete data are used to emulate real conditions in a beam and truss in different fault scenarios. Simulations show satisfactory performance in all cases studied. Therefore, this system can be used as suitable structural damage detection system. [ABSTRACT FROM AUTHOR]