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

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

Simple allelic-phenotype diversity and differentiation statistics for allopolyploids.

The analysis of genetic diversity within and between populations is a routine task in the study of diploid organisms. However, population genetic studies of polyploid organisms have been hampered by difficulties associated with scoring and interpreting molecular data. This occurs because the presence of multiple alleles at each locus often precludes the measurement of genotype or allele frequencies. In allopolyploids, the problem is compounded because genetically distinct isoloci frequently share alleles. As a result, analysis of genetic diversity patterns in allopolyploids has tended to rely on the interpretation of phenotype frequencies, which loses information available from allele composition. Here, we propose the use of a simple allelic-phenotype diversity statistic (H') that measures diversity as the average number of alleles by which pairs of individuals differ. This statistic can be extended to a population differentiation measure (F'ST), which is analogous to FST. We illustrate the behaviour of these statistics using coalescent computer simulations that show that F'ST behaves in a qualitatively similar way to FST, thus providing a useful way to quantify population differentiation in allopolyploid species