Numerical estimation and experimental verification of optimal parameter identification based on modern optimization of a three phase induction motor

The parameters of electric machines play a substantial role in the control system which, in turn, has a great impact on machine performance. In this paper, a proposed optimal estimation method for the electrical parameters of induction motors is presented. The proposed method uses the particle swarm optimization (PSO) technique. Further, it also considers the influence of temperature on the stator resistance. A complete experimental setup was constructed to validate the proposed method. The estimated electrical parameters of a 3.8-hp induction motor are compared with the measured values. A heat run test was performed to compare the effect of temperature on the stator resistance based on the proposed estimation method and the experimental measurements at the same conditions. It is shown that acceptable accuracy between the simulated results and the experimental measurements has been achieved

Dynamic Jiles–Atherton Model for Determining the Magnetic Power Loss at High Frequency in Permanent Magnet Machines

This paper describes a mathematical model for the dynamic magnetic power losses in the laminated steel stator of high frequency permanent magnet machines, such as Brushless DC (BLDC) Motors. The model presented is based on a utilization of the dynamic Jiles-Atherton model. Accurate dynamic BH curve fitting and magnetic power loss derivations have been achieved, where the calculated magnetic losses have shown around 95% accuracy from 5 Hz to 2000 Hz, over a flux density range of 1.0 T to 1.6 T. This approach has been applied to estimate the magnetic power loss of a small scale, high frequency (>10,000 rpm) BLDC motor, with calculated and measured losses being in close agreement.This is the author accepted manuscript. The final version is available from IEEE via 10.1109/TMAG.2014.2382594

Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies

© 1986-2012 IEEE.A dynamic particle swarm optimization with learning strategy (DPSO-LS) is proposed for key parameter estimation for permanent magnet synchronous machines (PMSMs), where the voltage-source inverter (VSI) nonlinearities are taken into account in the parameter estimation model and can be estimated simultaneously with other machine parameters. In the DPSO-LS algorithm, a novel movement modification equation with variable exploration vector is designed to effectively update particles, enabling swarms to cover large areas of search space with large probability and thus the global search ability is enhanced. Moreover, a Gaussian-distribution-based dynamic opposition-based learning strategy is developed to help the pBest jump out local optima. The proposed DPSO-LS can significantly enhance the estimator model accuracy and dynamic performance. Finally, the proposed algorithm is applied to multiple parameter estimation including the VSI nonlinearities of a PMSM. The performance of DPSO-LS is compared with several existing PSO algorithms, and the comparison results show that the proposed parameters estimation method has better performance in tracking the variation of machine parameters effectively and estimating the VSI nonlinearities under different operation conditions

Differential evolution schemes for speech segmentation: A comparative study

This paper presents a signal processing technique for segmenting short speech utterances into unvoiced and voiced sections and identifying points where the spectrum becomes steady. The segmentation process is part of a system for deriving musculoskeletal articulation data from disordered utterances, in order to provide training feedback. The functioning of the signal processing technique has been optimized by selecting the parameters of the model. The optimization has been carried out by testing and comparing multiple Differential Evolution implementations, including a standard one, a memetic one, and a controlled randomized one. Numerical results have also been compared with a famous and efficient swarm intelligence algorithm. For the given problem, Differential Evolution schemes appear to display a very good performance as they can quickly reach a high quality solution. The binomial crossover appears, for the given problem, beneficial with respect to the exponential one. The controlled randomization appears to be the best choice in this case. The overall optimized system proved to segment well the speech utterances and efficiently detect its uninteresting part

Lattice dynamical wavelet neural networks implemented using particle swarm optimisation for spatio-temporal system identification

Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNN), is introduced for spatiotemporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimisation (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the orthogonal projection pursuit algorithm, significant wavelet-neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated waveletneurons are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be redundant. In the second stage, an orthogonal least squares (OLS) algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet-neurons from the network. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet-neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework

Comparison between unipolar and bipolar single phase grid-connected inverters for PV applications

An inverter is essential for the interfacing of photovoltaic panels with the AC network. There are many possible inverter topologies and inverter switching schemes and each one will have its own relative advantages and disadvantages. Efficiency and output current distortion are two important factors governing the choice of inverter system. In this paper, it is argued that current controlled inverters offer significant advantages from the point of view of minimisation of current distortion. Two inverter switching strategies are explored in detail. These are the unipolar current controlled inverter and the bipolar current controlled inverter. With respect to low frequency distortion, previously published works provide theoretical arguments in favour of bipolar switching. On the other hand it has also been argued that the unipolar switched inverter offers reduced switching losses and generates less EMI. On efficiency grounds, it appears that the unipolar switched inverter has an advantage. However, experimental results presented in this paper show that the level of low frequency current distortion in the unipolar switched inverter is such that it can only comply with Australian Standard 4777.2 above a minimum output current. On the other hand it is shown that at the same current levels bipolar switching results in reduced low frequency harmonics