An intelligent power factor corrector for power system using artificial neural networks

An intelligent power factor correction approach based on artificial neural networks (ANN) is introduced. Four learning algorithms, backpropagation (BP), delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS), were used to train the ANNs. The best test results obtained from the ANN compensators trained with the four learning algorithms were first achieved. The parameters belonging to each neural compensator obtained from an off-line training were then inserted into a microcontroller for on-line usage. The results have shown that the selected intelligent compensators developed in this work might overcome the problems occurred in the literature providing accurate, simple and low-cost solution for compensation.     

A Novel Approach of Perturb and Observe Technique Adapted to Rapid Change of Environmental Conditions and Load

Abstract

The basic P&O technique fails to track the MPP when a rapid change in luminosity and/or temperature has occurred because it cannot distinguish between disturbance of PV voltage and rapid change in environmental conditions. This study presents drawbacks of the basic algorithm and proposes a new variant of P&O with three incremental steps to differentiate the two states mentioned above, thus avoiding divergences in the MPPT path. The suggested MPPT command consists of a modified P&O algorithm applied to a boost converter to overcome poor transient responses and thus optimize the efficiency of the entire PV system. The proposed tracker is built using a 60?W module and its performance is verified by simulations with MATLAB tools considering quick change in the solar irradiance, temperature and load. The test results demonstrate the feasibility and improved performance of the system. The modified P&O technique effectively tracks the MPP in various work situations and capable of increasing energy transfer efficiency by nearly 5%.     

A Comparative Study on Exergetic Performance Assessment for Drying of Broccoli Florets in Three Different Drying Systems

This article deals with the exergy analysis and evaluation of broccoli in three different drying systems. The effects of drying air temperature on the exergy destruction, exergy efficiency, and exergetic improvement potential of the drying process were investigated. The exergy destruction rate for the drying chamber increased with the rise in the drying air temperature at 1.5 m/s, both in the tray and the heat pump dryer. The highest exergy efficiency value was obtained as 90.86% in the fluid bed dryer in comparison to the other two drying systems and the improvement potential rate was the highest in the heat pump dryer during drying of broccoli at the drying air temperature of 45°C and the drying air velocity of 1.0 m/s.     

Context matters: The benefits and costs of expressing positive emotion among survivors of childhood sexual abuse.

Positive emotions promote adjustment to aversive life events. However, evolutionary theory and empirical research on trauma disclosure suggest that in the context of stigmatized events, expressing positive emotions might incur social costs. To test this thesis, the authors coded genuine (Duchenne) smiling and laughter and also non-Duchenne smiling from videotapes of late-adolescent and young adult women, approximately half with documented histories of childhood sexual abuse (CSA), as they described the most distressing event of their lives. Consistent with previous studies, genuine positive emotional expression was generally associated with better social adjustment two years later. However, as anticipated, CSA survivors who expressed positive emotion in the context of describing a past CSA experience had poorer long-term social adjustment, whereas CSA survivors who expressed positive emotion while describing a nonabuse experience had improved social adjustment. These findings suggest that the benefits of positive emotional expression may often be context specific. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Neurogenetic approach for the resource-constrained project scheduling problem

A variety of metaheuristic approaches have emerged in recent years for solving the resource-constrained project scheduling problem (RCPSP), a well-known NP-hard problem in scheduling. In this paper, we propose a Neurogenetic approach which is a hybrid of genetic algorithms (GA) and neural-network (NN) approaches. In this hybrid approach the search process relies on GA iterations for global search and on NN iterations for local search. The GA and NN search iterations are interleaved in a manner that allows NN to pick the best solution thus far from the GA pool and perform an intensification search in the solution's local neighborhood. Similarly, good solutions obtained by NN search are included in the GA population for further search using the GA iterations. Although both GA and NN approaches, independently give good solutions, we found that the hybrid approach gives better solutions than either approach independently for the same number of shared iterations. We demonstrate the effectiveness of this approach empirically on the standard benchmark problems of size J30, J60, J90 and J120 from PSPLIB.