Sigmoid function approximation for ANN implementation in FPGA devices

The objective of this work is the implementation of Artificial Neural Network on a FPGA board. This implementation aim is to contribute in the hardware integration solutions in the areas such as monitoring, diagnosis, maintenance and control of power system as well as industrial processes. Since the Simulink library provided by Xilinx, has all the blocks that are necessary for the design of Artificial Neural Networks except a few functions such as sigmoid function. In this work, an approximation of the sigmoid function in polynomial form has been proposed. Then, the sigmoid function approximation has been implemented on FPGA using the Xilinx library. Tests results are satisfactor

Sensorless speed field-oriented control of induction motor tacking core loss into account

In field-oriented controlled induction motor drives, the instantaneous rotor speed is measured using whether sensors or estimators. Since the basic Kalman filter is a state observer, its use in vector controlled schemes has received much attention. However, these schemes are based on the assumption that the existence of iron loss in the induction motor may be neglected. The paper shows the effect of iron loss on the extended Kalman filter performance that is designed on the basis of the classical dq model. Original simulation results are carried out to demonstrate this effect as well as the effectiveness of the suggested approach to minimise the speed estimation error without modifying the EKF's algorithm

A new algorithm applied to the evaluation of self excited induction generator performance

The paper presents the application of DIRECT algorithm to analyse the performances of the Self-excited induction generator. It is used to minimize the induction generator admittance yielding the solution which consists of the magnetizing reactance and the frequency. These parameters are the keys to find out the self excitation process requirements in terms of the prime mover speed, the capacitance and the load impedance and finally the output performances such as the voltage, output power, etc. A comparison with other powerful optimization algorithms is investigated to obtain DIRECT algorithm performance

Induction Motor Faults Detection Using a Statistical Procedure Based-Approach

This paper presents the application of a new technique based on the variance of three phase stator currents’ instantaneous variance (VIV-TPSC) to detect faults in induction motors. The proposed fault detection algorithm is based on computation of the confidence interval index (CI) at different load conditions. This index provides an estimate of the amount of error in the considered data and determines the accuracy of the computed statistical estimates. The algorithm offers the advantage of being able to detect faults, particularly broken rotor bars, independently of loading conditions. Moreover, the implementation of the algorithm requires only the calculation of the variance of the measured three-phase stator currents’ instantaneous variance. The discrimination between faulty and healthy operations is based on the adherence of VIV-TPSC value to the CI which is calculated after checking out that the variance of instantaneous variance is a random variable obeying to normal distribution law. Rotor and stator resistance values are not used in any part of the CI and VIV-TPSC calculations, giving the algorithm more robustness. The effectiveness and the accuracy of the proposed approach are shown under different faulty operations.</jats:p