This paper proposes a generalized space vector pulse width modulation (SVPWM) technique for hybrid multilevel inverters for minimizing total harmonic distortion (THD). The proposed method easily determines the actual location of the instantaneous reference space vector and the corresponding switching sequence of a multilevel inverter. The proposed algorithm offers a novel method for minimizing ...

The paper presents a space-vector pulse width modulation (SVPWM) inverter feeding a permanent-magnet synchronous motor (PMSM). The SVPWM inverter enables to feed the motor with a higher voltage with low harmonic distortions than the conventional sinusoidal PWM inverter. The control strategy of the inverter is the voltage / frequency control method, which is based on the space-vector modulation ...

This paper proposes a method of applying the Space Vector Modulation technique for Direct Torque Control (DTC) of a permanent magnet synchronous motor (PMSM) drive. By this method it is preserved the principle of the conventional DTC regarding the decoupled torque and flux control, while providing more flexibility for the inverter voltage utilization, in order to compensate the torque and flux ...

The aim of this study is to introduce a new methodology for isolation of ectopic rhythms of ambulatory electrocardiogram (ECG) holter data via appropriate statistical analyses imposing reasonable computational burden. First, the events of the ECG signal are detected and delineated using a robust wavelet-based algorithm. Then, using Binary Neyman-Pearson Radius test, an appropriate classifie...

This study presents a novel technique based on Support Vector Machine (SVM) for the classification of transient phenomena in power transformer. The SVM is a powerful method for statistical classification of data. The input data to this SVM for training comprises fault current and magnetizing inrush current. SVM classifier produces significant accuracy for classification of transient phenomena i...

The choice of an SVM kernel corresponds to the choice of a representation of the data in a feature space and, to improve performance, it should therefore incorporate prior knowledge such as known transformation invariances. We propose a technique which extends earlier work and aims at incorporating invariances in nonlinear kernels. We show on a digit recognition task that the proposed approach ...

The dimensionality of existing data make it difficult to deploy any information to identify features that discriminate between the classes of interest. Feature selection involves reducing the number of features, removes irrelevant, noisy and redundant data without significantly decreasing the prediction accuracy of the classifier. An efficient feature selection and classification technique for ...

In this paper, hybrid pulse width modulation algorithm for vector controlled induction motor drives is used to reduce the steady state ripple in current. Also the newly developed Hybrid PWM technique does not use angle and sector information and hence it simplifies the control algorithm. The conventional Space vector pulse width modulation (CSVPWM) algorithm gives inferior performance at higher...

In this paper a methodology for estimation in kernel-induced feature spaces is presented, making a link between the primal-dual formulation of Least Squares Support Vector Machines (LS-SVM) and classical statistical inference techniques in order to perform linear regression in primal space. This is done by computing a finite dimensional approximation of the kernel-induced feature space mapping ...

The algorithm and the results of a nonlinear detector using a machine learning technique called support vector machine (SVM) on an efficient modulation system with high data rate and low energy consumption is presented in this paper. Simulation results showed that the performance achieved by the SVM detector is comparable to that of a conventional threshold decision (TD) detector. The two detec...