Contingency-Constrained Optimal Power Flow Using Simplex-Based Chaotic-PSO Algorithm

This paper proposes solving contingency-constrained optimal power flow (CC-OPF) by a simplex-based chaotic particle swarm optimization (SCPSO). The associated objective of CC-OPF with the considered valve-point loading effects of generators is to minimize the total generation cost, to reduce transmission loss, and to improve the bus-voltage profile under normal or postcontingent states. The proposed SCPSO method, which involves the chaotic map and the downhill simplex search, can avoid the premature convergence of PSO and escape local minima. The effectiveness of the proposed method is demonstrated in two power systems with contingency constraints and compared with other stochastic techniques in terms of solution quality and convergence rate. The experimental results show that the SCPSO-based CC-OPF method has suitable mutation schemes, thus showing robustness and effectiveness in solving contingency-constrained OPF problems

Chaotic multi-objective optimization based design of fractional order PI{\lambda}D{\mu} controller in AVR system

In this paper, a fractional order (FO) PI{\lambda}D\mu controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing the trade-off between different design criteria are obtained for the PI{\lambda}D\mu and PID controller. A comparative analysis is done with respect to the standard PID controller to demonstrate the merits and demerits of the fractional order PI{\lambda}D\mu controller.Comment: 30 pages, 14 figure

Sensing cloud optimization to solve ED of units with valve-point effects and multi-fuels

In this paper a solution to an highly constrained and non-convex economical dispatch (ED) problem with a meta-heuristic technique named Sensing Cloud Optimization (SCO) is presented. The proposed meta-heuristic is based on a cloud of particles whose central point represents the objective function value and the remaining particles act as sensors "to fill" the search space and "guide" the central particle so it moves into the best direction. To demonstrate its performance, a case study with multi-fuel units and valve- point effects is presented

A Case Control Study of Nutrient Intake Deficiencies in Patients Taking Warfarin

Introduction We previously published the case of a woman taking warfarin who was found to have scurvy, a disease caused by a deficiency of vitamin C. This led us to hypothesize that patients taking warfarin who consume a diet limited in vitamin K rich foods may be at risk for other nutrient deficiencies. To test our hypothesis, we studied dietary nutrient intake in patients taking warfarin compared to patients with heart disease not taking warfarin. Methods The warfarin (n=59) and control groups (n=24) comprised convenience samples of patients with heart disease over age 60 years. Patients completed a three-day food diary and reported use of supplements. Results Based on diet history, the most common deficiencies were vitamin D (100% both groups), vitamin E (93% warfarin, 92% control), vitamin A (71% warfarin, 71% control), vitamin K (66% warfarin, 58% control), vitamin C (58 % warfarin, 46% control) and pantothenic acid (69% warfarin, 71% control) with no significant differences in intake deficiencies between warfarin and control groups. Conclusion All of our patients had nutritional intake deficiencies. This may be due to Appalachian dietary habits and not the low vitamin K diet. It seems prudent to recommend multivitamins, however, universal multivitamin supplementation has not been supported by randomized controlled trials. More study is needed to determine the reason for poor nutritional intake in our Appalachian population and to determine whether similar results are evident in a larger sample

Galafen : por una energía continua y renovada

Fil: Gaing, Nicolás. Universidad de San Andrés. Escuela de Negocios; Argentina.Berger, Gabrie