Direct torque control for dual three-phase induction motor drives

A direct torque control (DTC) strategy for dual three-phase induction motor drives is discussed in this paper. The induction machine has two sets of stator three-phase windings spatially shifted by 30 electrical degrees. The DTC strategy is based on a predictive algorithm and is implemented in a synchronous reference frame aligned with the machine stator flux vector. The advantages of the discussed control strategy are constant inverter switching frequency, good transient and steady-state performance, and low distortion of machine currents with respect to direct self-control (DSC) and other DTC schemes with variable switching frequency. Experimental results are presented for a 10-kW DTC dual three-phase induction motor drive prototype

Linear Model Predictive Control of Induction Machine

This article presents new control algorithm for induction machine based on linear model predictive control (MPC). Controller works in similar manners as field oriented control (FOC), but control is performed in stator coordinates. This reduces computational demands as Park’s transformation is absent and induction machine mathematical model in stator coordinates contains less nonlinear elements. Another aim of proposed controller was to achieve fast torque response

A survey of induction algorithms for machine learning

Central to all systems for machine learning from examples is an induction algorithm. The purpose of the algorithm is to generalize from a finite set of training examples a description consistent with the examples seen, and, hopefully, with the potentially infinite set of examples not seen. This paper surveys four machine learning induction algorithms. The knowledge representation schemes and a PDL description of algorithm control are emphasized. System characteristics that are peculiar to a domain of application are de-emphasized. Finally, a comparative summary of the learning algorithms is presented

Comparitive study of the influence of harmonic voltage distortion on the efficiency of induction machines versus line start permanent magnet machines

Induction machines have nearly reached their maximal efficiency. In order to further increase the efficiency the use of permanent magnets in combination with the robust design of the induction machine is being extensively researched. These so-called line start permanent magnet machines have an increased efficiency in sine wave conditions in respect to standard induction machines, however the efficiency of these machines is less researched under distorted voltage conditions. This paper compares the influence of harmonic voltage distortion and the phase angle of the harmonic content on the overall motor efficiency of line start permanent magnet machines and induction machines

Channel-wall limitations in the magnetohydrodynamic induction generator

Discussion of magnetohydrodynamic induction generator examines the machine in detail and materials problems influencing its design. The higher upper-temperature limit of the MHD system promises to be more efficient than present turbine systems for generating electricity

Efficient algorithms for decision tree cross-validation

Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementation of the technique is its computational overhead. In this paper we show that, for decision trees, the computational overhead of cross-validation can be reduced significantly by integrating the cross-validation with the normal decision tree induction process. We discuss how existing decision tree algorithms can be adapted to this aim, and provide an analysis of the speedups these adaptations may yield. The analysis is supported by experimental results.Comment: 9 pages, 6 figures. http://www.cs.kuleuven.ac.be/cgi-bin-dtai/publ_info.pl?id=3478

Rule-based Machine Learning Methods for Functional Prediction

We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.Comment: See http://www.jair.org/ for any accompanying file