Embedded DSP-based compact fuzzy system and its application for induction motor V/F control

Este trabalho apresenta uma metodologia de implementação de algoritmos com estratégias fuzzy para sistemas embarcados em processadores digitais de sinais, cujo propósito de aplicação consiste no controle escalar de motores de indução trifásicos. A estratégia de controle adotada reside no ajuste da amplitude e freqüência (V/f) do sinal fundamental da tensão de alimentação do motor de indução, que foi modulado por largura de pulso aplicado a um inversor trifásico. Para tanto, o sistema de controle fuzzy foi integralmente embarcado em um processador digital de sinal empregando-se técnicas de simplificação que visam à redução dos requisitos de memória e custo computacional. O desempenho do controlador foi avaliado experimentalmente sob condições de variação de torque de carga aplicado ao eixo do motor de indução trifásico e referência de velocidade. Análises comparativas com as técnicas de controle PI e PID foram também realizadas com o propósito de validação da metodologia proposta.This paper presents a methodology for the implementation of embedded fuzzy system algorithms to be built in digital signal processors. For the purpose of this application, the technique was applied for induction motor scalar speed control. The adopted control strategy is to adjust the amplitude and frequency (V/f) of fundamental supply voltage signal of induction motor, which is achieved by a three-phase pulse width modulation inverter. The fuzzy control system was therefore entirely embedded in DSP by applying simplification techniques, which aim at computational cost and memory requirements reduction. The controller performance in relation to load torque and speed reference variations was evaluated by experimental tests. A comparative analysis with conventional PI and PID controllers was also achieved.FAPESPCNP

Photovoltaic water pumping system for small power conventional AC pumps

The interest of photovoltaic (PV) water pumping systems with standard components is increasing as a low cost and independent solution over dedicated systems. However, one of the challenges of this alternative is the fact that small power systems to drive power pumps require a small number of PV modules and the PV string voltage is, usually, not enough to feed the frequency converter. This paper presents an approach to the drive, using a DC/DC converter with maximum power point tracking (MPPT). The overall system has been tested on a real experimental platform.info:eu-repo/semantics/publishedVersio

Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signal

Considering the classification of failures in electrical machines, the present paper aims to use supervised machine learning techniques in order to classify faults in electrical machines, using attributes from audio signals. In order to analyze data and recognize patterns, the considered supervised learning methods are: Bayesian Network, together with the BayesRule algorithm, Support Vector Machine and k-Nearest Neighbor. The performances and the results provided from these algorithms are then compared. The main contributions of this paper are the acquisition process of audio signals and the elaboration of Bayesian networks topologies and classifiers structures using the acquired signals, since the algorithms provide the generalization of the classification model by revealing the network structure. Also, the utilization of audio signals as input attributes to the classifiers is infrequent in the literature. The results show that the Support Vector Machine and k-Nearest Neighbor present a high accuracy. On the other hand, the Bayesian approach is advantageous due to the possibility of showing, through graph representations, the generalized structure to represent the trend of faults in real cases on industry applications.info:eu-repo/semantics/publishedVersio

Estimation of bearing fault severity in line-connected and inverter-fed three-phase induction motors

Producción CientíficaThis paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess classification accuracy and fault estimation, the models described in this paper are investigated by using Artificial Neural Networks models. The paper also provides process flowcharts and classification tables to present the prognostic models used to estimate the remaining useful life of a defective bearing. Experimental results confirmed the method robustness and provide an accurate diagnosis regardless of the bearing fault stage, motor speed, load level, and type of supply.CAPES (process BEX552269/2011-5)National Council for Scientific and Technological Development (grant #474290/2008-3, #473576/2011-2, #552269/2011-5, #307220/2016-8

Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors

Producción CientíficaThree-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.Consejo Nacional de Desarrollo Científico y Tecnológico - (processes 474290/2008-5, 473576/2011-2, 552269/2011-5, 201902/2015-0 and 405228/2016-3

Speed neural estimator for the three-phase induction motors

Este trabalho apresenta uma estratégia para a estimativa de velocidade do motor de indução trifásico baseada em redes neurais artificiais utilizando medidas de variáveis primárias como tensão e corrente. O uso de motores de indução trifásicos é uma constante em diversos setores industriais e de grande importância no cenário energético nacional. A maioria das metodologias de controle, acionamento e dimensionamento destes motores é fundamentada nas medidas de velocidade no eixo. Entretanto, a medida direta da velocidade compromete o sistema de controle e acionamento diminuindo sua robustez e aumentando o custo de implementação. Resultados de simulação e de ensaios experimentais para validação da proposta são também apresentados.This work presents an approach to estimate speed in induction motors based on artificial neural networks and using measurement of primary variables like voltage and current. The use of induction motors is very common in many industrial sectors and plays an important role in the national energetic scene. The methodologies used in control, start up and dimensioning of these motors are based on measure of the speed variable. However, the direct measure of this variable compromises the system control and start up of the machine, reducing its robustness and increasing the implementation costs. Simulation results and experimental data are presented to validate the proposed approach

Embedded DSP-Based Compact Fuzzy System and Its Application for Induction-Motor V/f Speed Control

This paper presents a compact embedded fuzzy system for three-phase induction-motor scalar speed control. The control strategy consists in keeping constant the voltage-frequency ratio of the induction-motor supply source. A fuzzy-control system is built on a digital signal processor, which uses speed error and speed-error variation to change both the fundamental voltage amplitude and frequency of a sinusoidal pulsewidth modulation inverter. An alternative optimized method for embedded fuzzy-system design is also proposed. The controller performance, in relation to reference and load-torque variations, is evaluated by experimental results. A comparative analysis with conventional proportional-integral controller is also achieved