Neural Network-Based Approach for Identification of the Harmonic Content of a Nonlinear Load in a Single-Phase System

In this paper an alternative method based on artificial neural networks is presented to determine harmonic components in the load current of a single-phase electric power system with nonlinear loads, whose parameters can vary so much in reason of the loads characteristic behaviors as because of the human intervention. The first six components in the load current are determined using the information contained in the time-varying waveforms. The effectiveness of this method is verified by using it in a single-phase active power filter with selective compensation of the current drained by an AC controller. The proposed method is compared with the fast Fourier transform.

A novel single-phase ZCS-PWM high-power-factor boost rectifier

This paper presents a novel single-phase high-power-factor (HPF) pulsewidth-modulated (PWM) boost rectifier featuring soft commutation of the active switches at zero current (ZC), It incorporates the most desirable properties of conventional PWM and soft-switching resonant techniques.The input current shaping is achieved with average current mode control and continuous inductor current mode.This new PWM converter provides ZC turn on and turn off of the active switches, and it is suitable for high-power applications employing insulated gate bipolar transistors (IGBT's),The principle of operation, the theoretical analysis, a design example, and experimental results from a laboratory prototype rated at 1600 W with 400-Vdc output voltage are presented. The measured efficiency and the power factor were 96.2% and 0.99%, respectively, with an input current total harmonic distortion (THD) equal to 3.94%, for an input voltage with THD equal to 3.8%, at rated load.

Novel high-power-factor ZCS-PWM preregulators

This paper introduces novel zero-current-switching (ZCS) pulsewidth-modulated (PWM) preregulators based on a new soft-commutation cell, suitable for insulated gate bipolar transistor applications. The active switches in these proposed rectifiers turn on in zero current and turn off in zero current-zero voltage. In addition, the diodes turn on in zero voltage and their reverse-recovery effects over the active switches are negligible. Moreover, based on the proposed cell, an entire family of de-to-de ZCS-PWM converters can be generated, providing conditions to obtain naturally isolated converters, for example, derived buck-boost, Sepic. and Zeta converters. The novel ac-to-dc ZCS-PWM boost and Zeta preregulators are presented in order to verify the operation of this soft-commutation cell, In order to minimize the harmonic contents of the input current, increasing the ac power factor, the average-current-mode control is used, obtaining preregulators with ac power factor near unity and high efficiency at wide load range. The principle of operation, theoretical analysis, design example, and experimental results from test units for the novel preregulators are presented. The new boost preregulator was designed to nominal values of 1.6 kW output power, 220 V(rms) input voltage, 400 V(dc) output voltage, and operating at 20 kHz. The measured efficiency and power factor of the new ZCS-PWM boost preregulator were 96.7% and 0,99, respectively, with an input current total harmonic distortion (THD) equal to 3.42% for an input voltage with THD equal to 1.61%, at rated load, the new ZCS-PWM Zeta preregulator was designed to voltage step-down operation, and the experimental results were obtained from a laboratory prototype rated at 500 W, 220 V(rm), input voltage, 110 V(dc) output voltage, and operating at 50 kHz. The measured efficiency of the new ZCS-PWM Zeta preregulator is approximately 96.9% and the input power factor is 0.98, with an input current THD equal to 19.07% while the input voltage THD is equal to 1.96%, at rated load.

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.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

An Improved Parameterization Technique for the Continuation Power Flow

Continuation methods have been long used in P-V curve tracing due to their efficiency in the resolution of ill-conditioned cases, with close to singular Jacobian matrices, such as the maximum loading point of power systems. Several parameterization techniques have been proposed to avoid matrix singularity and successfully solve those cases. This paper presents a simple geometric parameterization technique to overcome the singularity of the Jacobian matrix by the addition of a line equations located at the plane determined by a bus voltage magnitude and the loading factor. This technique enlarges the set of voltage variables that can be used to whole P-V curve tracing, without ill-conditioning problems and no need of parameter changes. Simulation results, obtained for large realistic Brazilian and American power systems, show that the robustness and efficiency of the conventional power flow are not only preserved but also improved.

Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Cellular Automaton Approach to Spatial Electric Load Forecasting

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Power flow for primary distribution networks considering uncertainty in demand and user connection

This paper presents a method for calculating the power flow in distribution networks considering uncertainties in the distribution system. Active and reactive power are used as uncertain variables and probabilistically modeled through probability distribution functions. Uncertainty about the connection of the users with the different feeders is also considered. A Monte Carlo simulation is used to generate the possible load scenarios of the users. The results of the power flow considering uncertainty are the mean values and standard deviations of the variables of interest (voltages in all nodes, active and reactive power flows, etc.), giving the user valuable information about how the network will behave under uncertainty rather than the traditional fixed values at one point in time. The method is tested using real data from a primary feeder system, and results are presented considering uncertainty in demand and also in the connection. To demonstrate the usefulness of the approach, the results are then used in a probabilistic risk analysis to identify potential problems of undervoltage in distribution systems. (C) 2012 Elsevier Ltd. All rights reserved.

Artificial neural networks for load flow and external equivalents studies

In this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits. An analysis of the input data required as well as the ANN architecture is presented. A multilayer perceptron trained with the Levenberg-Marquardt second order method is used. The proposed methodology was tested with the IEEE 30- and 57-bus, and an ill-conditioned 11-bus system. Normal operating conditions (base case) and several contingency situations including different load and generation scenarios have been considered. Simulation results show the excellent performance of the ANN for solving problems (a)-(d). (C) 2010 Elsevier B.V. All rights reserved.

A Parameterization Technique for the Continuation Power Flow Developed from the Analysis of Power Flow Curves

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The Effect of Power Bleaching Actived by Several Light Sources on Enamel Microhardness

The purpose of this study was to evaluate the influence of different light sources for in-office bleaching on surface microhardness of human enamel. One hundred and five blocks of third molars were distributed among seven groups. The facial enamel surface of each block was polished and baseline Knoop microhardness of enamel was assessed with a load of 25 g for 5 s. Subsequently, the enamel was treated with 35% hydrogen peroxide bleaching agent and photo-activated with halogen light (group A) during 38 s, LED (group B) during 360 s, and high intensity diode laser (group C) during 4 s. The groups D (38 s), E (360 s), and F (4 s) were treated with the bleaching agent without photo-activated. The control (group G) was only kept in saliva without any treatment. Microhardness was reassessed after 1 day of the bleaching treatment, and after 7 and 21 days storage in artificial saliva. The mean percentage and standard deviation of microhardness in Knoop Hardness Number were: A 97.8 +/- 13.1 KHN; B 95.5 +/- 12.7 KHN; C 84.2 +/- 13.6 KHN; D 128.6 +/- 20.5 KHN; E 133.9 +/- 14.2 KHN; F 123.9 +/- 14.2 KHN; G 129.8 +/- 18.8 KHN. Statistical analysis (p < 0.05; Tukey test) showed that microhardness percentage values were significantly lower in the groups irradiated with light when compared with the non-irradiated groups. Furthermore, the non-irradiated groups showed that saliva was able to enhance the microhardness during the measurement times. The enamel microhardness was decreased when light sources were used during the bleaching process and the artificial saliva was able to increase microhardness when no light was used.

Continuation load flow using automatically determined branch megawatt losses as parameters

The conventional Newton's method is considered to be inadequate for the computation of the maximum loading point (MLP) of power systems since: (i) it encounters difficulties in the vicinity of the MLP: and (ii) the load flow Jacobian matrix becomes singular at the MLP. It is well known that continuation methods are powerful and useful tools that are able to trace the solution PV curve without experiencing such diffculties. However, continuation methods require a parameterisation so that a modified, well conditioned set of load flow equations is obtained. In particular, the Jacobian matrix associated with this modified set of equations should not be singular at the MLP. The authors propose that the actual power losses in transmission branches (lines and transformers) are used to parameterise the approach. Specific procedures for the automatic determination of the most appropriate parameter (branch) are proposed. Such procedures include the utilisation of fast voltage-stability indices. Simulation results are presented to show that the proposed method is able to trace the whole solution PV curve very efficiently.

An efficient geometric parameterization technique for the continuation power flow

Continuation methods have been shown as efficient tools for solving ill-conditioned cases, with close to singular Jacobian matrices, such as the maximum loading point of power systems. Some parameterization techniques have been proposed to avoid matrix singularity and successfully solve those cases. This paper presents a new geometric parameterization scheme that allows the complete tracing of the P-V curves without ill-conditioning problems. The proposed technique associates robustness to simplicity and, it is of easy understanding. The Jacobian matrix singularity is avoided by the addition of a line equation, which passes through a point in the plane determined by the total real power losses and loading factor. These two parameters have clear physical meaning. The application of this new technique to the IEEE systems (14, 30, 57, 118 and 300 buses) shows that the best characteristics of the conventional Newton's method are not only preserved but also improved. (C) 2006 Elsevier B.V. All rights reserved.