An improved and fast UHPLC-PDA methodology for determination of L-ascorbic and dehydroascorbic acids in fruits and vegetables: evaluation of degradation rate during storage

This study provides a versatile validated method to determine the total vitamin C content, as the sum of the contents of L-ascorbic acid (L-AA) and dehydroascorbic acid (DHAA), in several fruits and vegetables and its degradability with storage time. Seven horticultural crops from two different origins were analyzed using an ultrahigh-performance liquid chromatographic–photodiode array (UHPLC-PDA) system, equipped with a new trifunctional high strength silica (100% silica particle) analytical column (100 mm×2.1 mm, 1.7 μm particle size) using 0.1% (v/v) formic acid as mobile phase, in isocratic mode. This new stationary phase, specially designed for polar compounds, overcomes the problems normally encountered in HPLC and is suitable for the analysis of large batches of samples without L-AA degradation. In addition, it proves to be an excellent alternative to conventional C18 columns for the determination of L-AA in fruits and vegetables. The method was fully validated in terms of linearity, detection (LOD) and quantification (LOQ) limits, accuracy, and inter/intraday precision. Validation experiments revealed very good recovery rate of 96.6±4.4% for L-AA and 103.1±4.8 % for total vitamin C, good linearity with r2-values >0.999 within the established concentration range, excellent repeatability (0.5%), and reproducibility (1.6%) values. The LOD of the method was 22 ng/mL whereas the LOQ was 67 ng/mL. It was possible to demonstrate that L-AA and DHAA concentrations in the different horticulture products varied oppositely with time of storage not always affecting the total amount of vitamin C during shelf-life. Locally produced fruits have higher concentrations of vitamin C, compared with imported ones, but vegetables showed the opposite trend. Moreover, this UHPLC-PDA methodology proves to be an improved, simple, and fast approach for determining the total content of vitamin C in various food commodities, with high sensitivity, selectivity, and resolving power within 3 min of run analysis.

Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Thermoeconomic analysis method for optimization of combined heat and power systems

This article presents an thermoeconomic analysis of cogeneration plants, applied as a rational technique to produce electric power and saturated steam. The aim of this new methodology is the minimum exergetic manufacturing cost (EMC), based on the Second Law of Thermodynamics. The decision variables selected for the optimization are the pressure and the temperature of the steam leaving the boiler in the case of using steam turbine, and the pressure ratio, turbine exhaust temperature and mass flow in the case of using gas turbines. The equations for calculating the capital costs of the components and products are formulated as a function of these decision variables. An application of the method using real data of a multinational chemical industry located in São Paulo state is presented. The conditions which establish the minimum cost are presented as finals conclusions.

Load compensation in four-wire electrical power systems

This work describes a methodology for power factor control and correction of the unbalanced currents in four-wire electric circuits. The methodology is based on the insertion of two compensation networks, one wye-grounded neutral and another in delta, in parallel to the load. The mathematical development has been proposed in previous work [3]. In this paper, however, the methodology was adapted to accept different power factors for the system to be compensated. on the other hand, the determination of the compensation susceptances is based on the instantaneous values of the load currents. The results are obtained using the MatLab - Simulink environment.

Sensitivity analysis by neural networks applied to power systems transient stability

This work presents a procedure for transient stability analysis and preventive control of electric power systems, which is formulated by a multilayer feedforward neural network. The neural network training is realized by using the back-propagation algorithm with fuzzy controller and adaptation of the inclination and translation parameters of the nonlinear function. These procedures provide a faster convergence and more precise results, if compared to the traditional back-propagation algorithm. The adaptation of the training rate is effectuated by using the information of the global error and global error variation. After finishing the training, the neural network is capable of estimating the security margin and the sensitivity analysis. Considering this information, it is possible to develop a method for the realization of the security correction (preventive control) for levels considered appropriate to the system, based on generation reallocation and load shedding. An application for a multimachine power system is presented to illustrate the proposed methodology. (c) 2006 Elsevier B.V. All rights reserved.

Neural network based on adaptive resonance theory with continuous training for multi-configuration transient stability analysis of electric power systems

This work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way. The Euclidean ARTMAP version provides more accurate and faster solutions, when compared to the fuzzy ARTMAP configuration. Three steps are necessary for the network working, training, analysis and continuous training. The training step requires much effort (processing) while the analysis is effectuated almost without computational effort. The proposed network allows approaching several topologies of the electric system at the same time; therefore it is an alternative for real time transient stability of electric power systems. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines. (C) 2010 Elsevier B.V. All rights reserved.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Simulation Tools of DC-DC Converters for Power Electronics Education

This paper presents interactive simulation tools to support the enhancement of power electronics education considering DC to DC converters. These interactive tools make use of the benefits of Java language to provide a dynamic and interactive visualization of simulations in steady-state for idealized non-isolated DC to DC converters. Additionally, this paper discusses the features and the usage of the interactive educational tools to serve as a first design tool for the laboratory experiments in the power electronics courses. In this way, some results were confronted with a well-known simulator package and with experimental results in order to validate the developed interactive simulation tools.

Fault section estimation in electric power systems using an optimization immune algorithm

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Planning and Projects of Secondary Electric Power Distribution Systems

This paper proposes a methodology to achieve integrated planning and projects for secondary distribution circuits. The planning model is formulated as a mixed integer nonlinear programming problem (MINLP). In order to resolve this problem, a tabu search (TS) algorithm is used, with a neighborhood structure developed to explore the physical characteristics of specific geographies included in the planning and expansion of secondary networks, thus obtaining effective solutions as well as low operating costs and investments. The project stage of secondary circuits consists of calculating the mechanical efforts to determine the support structures of the primary and secondary distribution systems and determining the types of structures that should be used in the system according to topological and electrical parameters of the network and, therefore, accurately assessing the costs involved in the construction and/or reform of secondary systems. A constructive heuristic based on information of the electrical and topological conditions between the medium voltage and low voltage systems is used to connect the primary systems and secondary circuits. The results obtained from planning and design simulations of a real secondary system of electric energy distribution are presented.

Primary power distribution systems planning taking into account reliability, operation and expansion costs

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Thermoeconomic analysis method for optimization of combined heat and power systems. Part I

This article presents a thermoeconomic analysis of cogeneration plants, applied as a rational technique to produce electric power and saturated steam. The aim of this new methodology is the minimum Exergetic Production Cost (EPC), based on the Second Law of Thermodynamics. The variables selected for the optimization are the pressure and the temperature of the steam leaving the boiler in the case of using steam turbine, and the pressure ratio, turbine exhaust temperature and mass flow in the case of using gas turbines. The equations for calculating the capital costs of the components and products are formulated as a function of these decision variables. An application of the method using real data of a multinational chemical industry located in São Paulo state is presented. The conditions which establish the minimum cost are presented as final output. (C) 2003 Elsevier Ltd. All rights reserved.

A W-matrix methodology for solving sparse network equations on multiprocessor computers

This paper describes a methodology for solving efficiently the sparse network equations on multiprocessor computers. The methodology is based on the matrix inverse factors (W-matrix) approach to the direct solution phase of A(x) = b systems. A partitioning scheme of W-matrix , based on the leaf-nodes of the factorization path tree, is proposed. The methodology allows the performance of all the updating operations on vector b in parallel, within each partition, using a row-oriented processing. The approach takes advantage of the processing power of the individual processors. Performance results are presented and discussed.

Applying a new methodology to verify transmission line model performance - the equivalent impedance test

The objective of this paper is to present a methodology to analyze a transmission line model used in electromagnetic transitory simulators, called equivalent impedance test. Initially the definition of equivalent impedance reference test is shown. Soon after this methodology is applied to a transmission line model, the Quasi-Modes model. The studies were accomplished in a hypothetical non-transposed three-phase transmission fine of 440 kV. The line length is 500 km, and it was modeled through cascades of pi-circuits (with 50 pi's circuits, each with 10 km length).