A. Goedtel

Embedded DSP-Based Compact Fuzzy System and Its Application for Induction-Motor

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.

V/f

Many electronic drivers for the induction motor control are based on sensorless technologies. The proposal of this work is to present an alternative approach of speed estimation, from transient to steady state, using artificial neural networks. The inputs of the network are the RMS voltage, current and speed estimated of the induction motor feedback to the input with a delay of n samples. Simulation results are also presented to validate the proposed approach

Speed Control

The induction motor is considered one of the most important elements in manufacturing processes. The use of strategies based on intelligent systems capable to classify the presence or absence of failures and also to determine its origin for the diagnosis and faults prediction is widely investigated in three phase induction motors. Thus, the aim of this paper is to present a methodology of bearing failures classification based on artificial neural networks, by using voltage and electric currents values in the time domain. Experimental results collected at real industrial process are presented to validate this proposal.

Recurrent Neural Network for Induction Motor Speed Estimation in Industry Applications

In this paper a method for the determination of part of the current harmonic components for the selective compensation harmonic by single-phase active power filter is presented. The non-linear load is composed by an AC controller with variable resistive load. The first six components are identified through artificial neural network. The effectiveness of the proposed method and its application in the single-phase active power filters with selective harmonic compensation are verified. Simulation results are presented to validate the proposed approach.

Neural approach for bearing fault classification in induction motors by using motor current and voltage

Induction motors are widely used in several industrial sectors. However, the selection of induction motors is often inaccurate because, in most cases, the load behavior in the shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for dimensioning induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. The potential of this approach is the simple hardware implementation since the methodology does not require torque sensor nor powerful computational processors. Simulation results are also presented to validate the proposed approach.

Harmonic Detection Based on Artificial Neural Networks for Current Distortion Compensation

The induction motor has been widely used in various industrial applications. Thus, several studies have presented strategies for the diagnosis and prediction of failures in these motor. One strategy used recently is based on intelligent systems, in particular, artificial neural networks. The purpose of this paper is to present an alternative tool to traditional methods for detection of bearing failures using on a perceptron network with signal analysis in time domain. Experimental results are presented to validate the proposal.

Uma abordagem neural para estimação de conjugado em motores de indução

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents a modified Hopfield architecture, which has equilibrium points representing the solution of the problems considered, i.e, dynamic programming problems and bipartite graph optimization. The internal parameters of the network have been computed using the valid-subspace technique. This method allows us to define a subspace, which contains only those vectors that represent feasible solutions to the problem analyzed. It has also been demonstrated that with appropriately set parameters, the network confines its output to this subspace, thus ensuring convergence to a valid solution. Simulation results and comparative analyses with other methods are presented to validate the proposed approach.

Neural approach for bearing fault detection in three phase induction motors

The semiconductor switches are presented in many applications and they can be considered the main source of harmonic distortion presented in the electrical power system. The use of filters - active or passive - has played an important role in order to minimize the harmonic effects injected in the power system. The proposal of this work is to present an alternative approach to estimate the harmonic content of a single-phase system with non-linear loads. It uses artificial neural networks to determine the compensation current. The system is composed of AC single-phase controllers and parallel active power filter. Simulation results are presented to validate the proposed approach

The modified Hopfield architecture applied in dynamic programming problems and bipartite graph optimization

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.

Compensation Current of Active Power Filter Generated by Artificial Neural Network Approach

The multilayer perceptron network has become one of the most used in the solution of a wide variety of problems. The training process is based on the supervised method, where the inputs are presented to the neural network and the output is compared with a desired value. However, the algorithm presents convergence problems when the desired output of the network has small slope in the discrete time samples or the output is a quasi-constant value. The proposal of this paper is presenting an alternative approach to solve this convergence problem with a pre-conditioning method of the desired output data set before the training process and a post-conditioning when the generalization results are obtained. Simulations results are presented in order to validate the proposed approach.