2006
DOI: 10.1109/tec.2005.853766
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Speed Sensorless Estimation of AC Induction Motors Using the Fast Orthogonal Search Algorithm

Abstract: This paper presents a method of estimating the speed of an induction motor using a measurement of the stator current. Speed-induced current harmonics are identified in the stator current using the fast orthogonal search (FOS) algorithm. The frequencies of these estimated harmonics are in turn used to estimate the speed of the motor given the number of rotor slots in the motor. Several optimizations of the FOS algorithm are presented to allow for real-time performance on an embedded digital signal processor. Ex… Show more

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Cited by 18 publications
(20 citation statements)
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“…FOS algorithm has a lot of practical applications including spectral model estimations, time series analysis and non-linear system control [19][20][21][22], and the proposed algorithm is based on FOS algorithm. The main goal of the proposed algorithm is to create a functional expansion of an input y(n) by choosing the best basis functions from all candidate functions P k (n), in order to minimize the mean squared error (MSE) between y(n) and the functional expansion.…”
Section: High Resolution Carrier Frequency Estimationmentioning
confidence: 99%
“…FOS algorithm has a lot of practical applications including spectral model estimations, time series analysis and non-linear system control [19][20][21][22], and the proposed algorithm is based on FOS algorithm. The main goal of the proposed algorithm is to create a functional expansion of an input y(n) by choosing the best basis functions from all candidate functions P k (n), in order to minimize the mean squared error (MSE) between y(n) and the functional expansion.…”
Section: High Resolution Carrier Frequency Estimationmentioning
confidence: 99%
“…The FOS algorithm [7,8,9] is a nonlinear technique that allows fitting of an input function y(n) of length N to a functional expansion given by (2) where p m (n) are arbitrary functions, a m are the weights of the functional expansion, and e(n) is the residual error of the model.…”
Section: Fos Algorithmmentioning
confidence: 99%
“…Computer-based real-time applications using fieldprogrammable gate array (FPGA) based hardware present attractive possibilities for enhancing real-time performance of electric drive systems. Compared to microcontroller systems traditionally employed to enable real-time applications in electric drives [1][2][3][4], FPGAs generally provide the advantages of a significantly more flexible architecture with unconstrained input/output interfaces, high compatibility levels across different platforms and increased processing speed. While realtime FPGA based solutions have been researched for implementation of drive control algorithms [5][6] they received significantly less attention where drive parameter estimation techniques are concerned [1][2][3][4].…”
Section: Introductionmentioning
confidence: 99%