Power converter topologies for wind energy conversion systems: Integrated modeling,control strategy and performance simulation

This paper presents new integrated model for variable-speed wind energy conversion systems, considering a more accurate dynamic of the wind turbine, rotor, generator, power converter and filter. Pulse width modulation by space vector modulation associated with sliding mode is used for controlling the power converters. Also, power factor control is introduced at the output of the power converters. Comprehensive performance simulation studies are carried out with matrix, two-level and multilevel power converter topologies in order to adequately assert the system performance. Conclusions are duly drawn.     

Influence of the drying route on the depolymerization and properties of chitosan

In this work, a chitosan sample with a high degree of deacetylation (DD >95%) obtained from freshwater shrimp shells was subjected to drying processes in an electric oven and by supercritical CO₂. The results indicated that drying chitosan particles with supercritical CO₂ resulted in a very significant increase in specific surface area and pore volume, and also increased the material's crystallinity index. This drying route led to a more than 10‐fold reduction in viscosimetric molecular weight (from 35.3 to 3.0 kDa), indicating that the physical drying process caused the chitosan to depolymerize, which usually occurs by enzymatic and chemical methods, according to the literature. Low molecular weight chitosan is essential for some applications in the field of biomedicine (drug delivery for example); hence, drying via the CO₂ route can be considered a promising technique for the production of high value‐added materials with applications in this area. POLYM. ENG. SCI., 55:1969–1976, 2015. © 2014 Society of Plastics Engineers     

Reducing Oxidation of Foods Through Antioxidant Active Packaging Based on Ethyl Vinyl Alcohol and Natural Flavonoids

The development of antioxidant active packaging systems is attracting considerable attention as one of the preferred emerging technologies for reducing the incidence of lipid peroxidation. This work presents the use of ethylene vinyl alcohol copolymer films containing two natural flavonoids, catechin and quercetin, to reduce the oxidation of food. In a series of experiments, these materials showed their ability to reduce the presence of hydroxyl radicals in the package headspace. Packaging fried peanuts in bags manufactured with these active films resulted in a large reduction in the presence of hexanal, a compound produced during peroxidation of the unsaturated fat in peanuts. The results indicated that the materials actively reduced the presence of radical oxidative species although the antioxidants are not released into the food. On exposing sunflower oil to the films, the peroxide values obtained showed that the films actively protected the oil; because of the higher solubility of quercetin in this food product as well as the higher antioxidant capacity, the samples containing this flavonoid were more efficient. Industrial relevance: Active packaging is receiving considerable attention as an emerging technology that can be used to improve the quality and the stability of food, reducing the direct addition of chemicals and the need for changes in formulation. The results of this study show that it is possible to reduce food oxidation without adding antioxidants to the food. The films obtained can be used to protect any type of food, including dry or fatty products. Copyright © 2012 John Wiley & Sons, Ltd.     

A systematic review of software fault prediction studies

This paper provides a systematic review of previous software fault prediction studies with a specific focus on metrics, methods, and datasets. The review uses 74 software fault prediction papers in 11 journals and several conference proceedings. According to the review results, the usage percentage of public datasets increased significantly and the usage percentage of machine learning algorithms increased slightly since 2005. In addition, method-level metrics are still the most dominant metrics in fault prediction research area and machine learning algorithms are still the most popular methods for fault prediction. Researchers working on software fault prediction area should continue to use public datasets and machine learning algorithms to build better fault predictors. The usage percentage of class-level is beyond acceptable levels and they should be used much more than they are now in order to predict the faults earlier in design phase of software life cycle.     

A Comparison between the Tikhonov and the Bayesian Approaches to Calculate Regularisation Matrices

Regularisation is a well-known technique for working with ill-posed and ill-conditioned problems that have been explored in a variety of different areas, including Bayesian inference, functional analysis, optimisation, numerical analysis and connectionist systems. In this paper we present the equivalence between the Bayesian approach to the regularisation theory and the Tikhonov regularisation into the function approximation theory framework, when radial basis functions networks are employed. This equivalence can be used to avoid expensive calculations when regularisation techniques are employed.     

Effect of processing parameters on sunflower phosphatidylcholine‐enriched fractions extracted with aqueous ethanol

Lecithins are widely used in the food industry because of their multifunctional characteristics. Fractionation of the original mixture of phospholipids in lecithin is desirable for certain applications. The influence of ethanol/water mixtures (90 : 10 to 96 : 4) and other operative conditions (temperature 35–65 °C, incubation time 30–90 min, solvent/lecithin ratio 2 : 1, 3 : 1) on the extraction of phosphatidylcholine (PC)‐enriched fractions of sunflower lecithin (a non‐GMO product) was investigated. Yield % and phospholipid composition of the enriched PC fractions as well as the residue were determined. The percent extraction coefficient of each phospholipid (E_PC, E_PE and E_PI) in the enriched PC fraction was calculated. Values of E_PC varied from 6.5 (35 °C, 30 min, 2 : 1, 90 : 10) to 52.6 (65 °C, 90 min, 3 : 1, 96 : 4). High temperature and long incubation time produced a significant increase of this coefficient (p <0.05) while a high water content in the ethanolic mixture resulted in a considerable decrease in PC extraction. E_PI (<3%) values showed the high insolubility of phosphatidylinositol. Statistical analysis and response surface methodology evidenced the influence of the different variables on the extraction of PC‐enriched fractions at laboratory scale.     

Analysis of wind power generation and prediction using ANN: A case study

Many developing nations, such as India have embarked upon wind energy programs for areas experiencing high average wind speeds throughout the year. One of the states in India that is actively pursuing wind power generation programs is Tamil Nadu. Within this state, Muppandal area is one of the identified regions where wind farm concentration is high. Wind energy engineers are interested in studies that aim at assessing the output of wind farms, for which, artificial intelligence techniques can be usefully adapted. The present paper attempts to apply this concept for assessment of the wind energy output of wind farms in Muppandal, Tamil Nadu (India). Field data are collected from seven wind farms at this site over a period of 3 years from April 2002 to March 2005 and used for the analysis and prediction of power generation from wind farms. The model has been developed with the help of neural network methodology. It involves three input variables—wind speed, relative humidity and generation hours and one output variable-energy output of wind farms. The modeling is done using MATLAB toolbox. The model accuracy is evaluated by comparing the simulated results with the actual measured values at the wind farms and is found to be in good agreement.     

REMOVING SINGULARITIES AND ASSESSING UNCERTAINTIES IN TWO EFFICIENT GROSS ERROR COLLECTIVE COMPENSATION METHODS

Two collective estimation strategies, the Unbiased Estimation Technique (Rollins and Davis, 1992) and the recursive Generalized Likelihood Ratio (Keller et at.,1994), have been shown lo be very efficient in detecting and estimating multiple gross errors in linear process systems. However, these strategies run into singularities and uncertainties that prevent them from being used in automatic schemes. This paper uses a recently presented theory on gross error equivalency to explain when and how these singularities and uncertainties take place. The procedures presented by these two methods are modified to prevent the singularities from appearing and allowing their automatic implementation.