Load torque estimation in induction motors using artificial neural networks

The induction motors are largely used in several industry sectors. The dimensioning of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this paper is to use artificial neural networks as tool for dimensioning of induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.

Load Characterization and Revenue Metering Under Non-Sinusoidal and Asymmetrical Operation

This paper proposes an approach to load characterization and revenue metering, which accounts for the influence of supply deterioration and line impedance. It makes use of the Conservative Power Theory and aims at characterizing the load from the measurements done at the point of common coupling. Despite the inherent limitations of a single-point measurement, the proposed methodology enables evaluation of power terms, which clarify the effects of reactivity, asymmetry and distortion, and attempts to depurate the power consumption accounted to the load from those terms deriving from supply nonidealities.

Distributed, Agent-Based Intelligent System for Demand Response Program Simulation in Smart Grids

A distributed, agent-based intelligent system models and simulates a smart grid using physical players and computationally simulated agents. The proposed system can assess the impact of demand response programs.

Neural network based estimation of torque in induction motors for real-time applications

Induction motors are largely used in several industry sectors. The selection of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this article is to use artificial neural networks for torque estimation with the purpose of best selecting the induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Since proposed approach estimates the torque behavior from the transient to the steady state, one of its main contributions is the potential to also be implemented in control schemes for real-time applications. Simulation results are also presented to validate the proposed approach.

Uma abordagem neural para estimação de conjugado em motores de indução

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

Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.

MODELING OF SOIL LOAD-BEARING CAPACITY AS A FUNCTION OF SOIL MECHANICAL RESISTANCE TO PENETRATION

Estimation of soil load-bearing capacity from mathematical models that relate preconsolidation pressure (σp) to mechanical resistance to penetration (PR) and gravimetric soil water content (U) is important for defining strategies to prevent compaction of agricultural soils. Our objective was therefore to model the σp and compression index (CI) according to the PR (with an impact penetrometer in the field and a static penetrometer inserted at a constant rate in the laboratory) and U in a Rhodic Eutrudox. The experiment consisted of six treatments: no-tillage system (NT); NT with chiseling; and NT with additional compaction by combine traffic (passing 4, 8, 10, and 20 times). Soil bulk density, total porosity, PR (in field and laboratory measurements), U, σp, and CI values were determined in the 5.5-10.5 cm and 13.5-18.5 cm layers. Preconsolidation pressure (σp) and CI were modeled according to PR in different U. The σp increased and the CI decreased linearly with increases in the PR values. The correlations between σp and PR and PR and CI are influenced by U. From these correlations, the soil load-bearing capacity and compaction susceptibility can be estimated by PR readings evaluated in different U.

Modeling field load tests in lateritic unsaturated soil

Many models for unsaturated soil have been developed in the last years, accompanying the development of experimental techniques to deal with such soils. The benchmark of the models for unsaturated soil can be assigned to the Barcelona Basic Model (BBM) now incorporated in some codes such as the CODE_BRIGHT. Most of those models were validated considering limited laboratory test results and not much validation is available considering real field problems. This paper presents modeling results of field plate load tests performed under known suction on a lateritic unsaturated soil. The required input data were taken from laboratory tests performed under suction control. The modeling nicely reproduces field tests allowing appreciating the influence of soil suction on the stress-settlement curve. In addition, wetting induced or collapse settlements were calculated from field tests and were nicely duplicated by the numerical analysis performed.

Failure behavior modeling of slender reinforced concrete columns subjected to eccentric load

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network

Neuroimaging studies have consistently shown that working memory (WM) tasks engage a distributed neural network that primarily includes the dorsolateral prefrontal cortex, the parietal cortex, and the anterior cingulate cortex. The current challenge is to provide a mechanistic account of the changes observed in regional activity. To achieve this, we characterized neuroplastic responses in effective connectivity between these regions at increasing WM loads using dynamic causal modeling of functional magnetic resonance imaging data obtained from healthy individuals during a verbal n-back task. Our data demonstrate that increasing memory load was associated with (a) right-hemisphere dominance, (b) increasing forward (i.e., posterior to anterior) effective connectivity within the WM network, and (c) reduction in individual variability in WM network architecture resulting in the right-hemisphere forward model reaching an exceedance probability of 99% in the most demanding condition. Our results provide direct empirical support that task difficulty, in our case WM load, is a significant moderator of short-term plasticity, complementing existing theories of task-related reduction in variability in neural networks. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.

Modeling of functionally graded piezoelectric ultrasonic transducers

The application of functionally graded material (FGM) concept to piezoelectric transducers allows the design of composite transducers without interfaces, due to the continuous change of property values. Thus, large improvements can be achieved, as reduction of stress concentration, increasing of bonding strength, and bandwidth. This work proposes to design and to model FGM piezoelectric transducers and to compare their performance with non-FGM ones. Analytical and finite element (FE) modeling of FGM piezoelectric transducers radiating a plane pressure wave in fluid medium are developed and their results are compared. The ANSYS software is used for the FE modeling. The analytical model is based on FGM-equivalent acoustic transmission-line model, which is implemented using MATLAB software. Two cases are considered: (i) the transducer emits a pressure wave in water and it is composed of a graded piezoceramic disk, and backing and matching layers made of homogeneous materials; (ii) the transducer has no backing and matching layer; in this case, no external load is simulated. Time and frequency pressure responses are obtained through a transient analysis. The material properties are graded along thickness direction. Linear and exponential gradation functions are implemented to illustrate the influence of gradation on the transducer pressure response, electrical impedance, and resonance frequencies. (C) 2009 Elsevier B. V. All rights reserved.

Viral Load Predicts New World Health Organization Stage 3 and 4 Events in HIV-Infected Children Receiving Highly Active Antiretroviral Therapy, Independent of CD4 T Lymphocyte Value

Background. Many resource-limited countries rely on clinical and immunological monitoring without routine virological monitoring for human immunodeficiency virus (HIV)-infected children receiving highly active antiretroviral therapy (HAART). We assessed whether HIV load had independent predictive value in the presence of immunological and clinical data for the occurrence of new World Health Organization (WHO) stage 3 or 4 events (hereafter, WHO events) among HIV-infected children receiving HAART in Latin America. Methods. The NISDI (Eunice Kennedy Shriver National Institute of Child Health and Human Development International Site Development Initiative) Pediatric Protocol is an observational cohort study designed to describe HIV-related outcomes among infected children. Eligibility criteria for this analysis included perinatal infection, age ! 15 years, and continuous HAART for >= 6 months. Cox proportional hazards modeling was used to assess time to new WHO events as a function of immunological status, viral load, hemoglobin level, and potential confounding variables; laboratory tests repeated during the study were treated as time-varying predictors. Results. The mean duration of follow-up was 2.5 years; new WHO events occurred in 92 (15.8%) of 584 children. In proportional hazards modeling, most recent viral load 15000 copies/mL was associated with a nearly doubled risk of developing a WHO event (adjusted hazard ratio, 1.81; 95% confidence interval, 1.05-3.11; P = 033), even after adjustment for immunological status defined on the basis of CD4 T lymphocyte value, hemoglobin level, age, and body mass index. Conclusions. Routine virological monitoring using the WHO virological failure threshold of 5000 copies/mL adds independent predictive value to immunological and clinical assessments for identification of children receiving HAART who are at risk for significant HIV-related illness. To provide optimal care, periodic virological monitoring should be considered for all settings that provide HAART to children.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.