M. Gomez-Gonzalez

Binary Particle Swarm Optimization for Optimization of Photovoltaic Generators in Radial Distribution Systems Using Probabilistic Load Flow

Abstract This article shows that technical constraints must be considered in radial distribution networks, where voltage regulation is one of the primary problems in distributed generation photovoltaic systems. Loads and distributed generation production are modeled as random variables. Results prove that the proposed method can be applied for keeping voltages within desired limits at all load buses of a photovoltaic grid-connected system. To evaluate the performance of a photovoltaic system, this article has developed a probabilistic model that takes into account the random nature of solar irradiance and load. This work introduces a new method utilizing discrete particle swarm optimization and probabilistic radial load flow. Computer simulation reduction demonstrates better performance of the new probabilistic load flow in comparison to the Monte Carlo simulation. Acceptable solutions are reached in a smaller number of iterations. Therefore, convergence is rapidly attained, and computational cost is low enough for that required for Monte Carlo simulation.

A Method for Reliability Optimization of Distributed Generation Using Meta-Heuristic and Probabilistic Techniques

Abstract—This article shows a method to optimize the reliability of an electric power system by the introduction of distributed generation. The reliability index of the system is determined as the failure probability of the system. A hybrid method that uses a binary shuffled frog-leaping algorithm and probabilistic load flow is proposed to search a large number of combinations for location and size of biomass power plants and photovoltaic units. The combination with the best performance minimizes the reliability index (probability of failure) of the system.

Personalized e-learning using shuffled frog-leaping algorithm

One of the main problems related with the design of e-learning is the current composition approaches do not support “personalized-learning”, that is, not take into account the difference in the prior knowledge of the learner and his learning ability. In order to provide solution for this problem, various e-course composition approaches have been proposed to use various techniques like Genetic Algorithm and Particle swarm optimization. This paper proposes an improved personalized e-course composition approach using shuffled frog-leaping algorithm (SFLA). The results of the simulations performed demonstrate that the proposed approach is a good solution to the problem raised. In addition, the method is compared with genetic algorithms and particle swarm optimization.