Conventionally, parameters of the Induction Motor (IM) are determined using the standard noload and locked rotor test. Performing the no-load test is simple and involved running the machine uncoupled to a load, while measuring the power, voltage, current and shaft speed at different voltage test points. On the other hand, the locked rotor test requires full control of the rotor mechanically in the locked condition before measurements are taken. This paper presents a method for estimating the parameters of IMs without the need for the no-load and locked rotor tests. The method is based on optimization approach using a relatively new swarm based algorithm called the Artificial Bee Colony (ABC) optimization. Two different equivalent circuits are implemented for the parameter estimation scheme; one with parallel and the other with series magnetization circuit. Parameters of a standard 7.5kW IM are estimated using the measured and estimated stator current, input and output power and the power factor. Based on the experimental results obtained, the optimization method using the ABC algorithm gave accurate estimates of the IM parameters when compared to the reference parameters determined using the IEEE standard 112-2004. The maximum errors of -13.730% and 2.249% are obtained for the parallel and series equivalent circuits respectively.

Several methods have been used to estimate the parameters of induction machines. The basic method is the standard no-load and block rotor test. Although accurate results are obtained using this method; however, performing the locked rotor test is difficult, requiring full control of the voltage by using appropriate instrument to mechanically secure the rotor in the locked condition. Therefore, in this paper, a method requiring only a no-load test to extract the parameters of the induction machine is presented. The proposed method is based on the modification of the third impedance calculation of the IEEE standard 112. To validate the proposed method, parameters of a standard 7.5kW induction machine are estimated. Based on the experimental results, the maximum recorded error in the parameter estimation is less than -2.881% when compared to the reference parameters obtained from the conventional no-load and blocked rotor test.

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In this paper, we propose an efficient algorithm for removing salt and pepper noise in images. The process of denoising is implemented in two stages: noise detection followed by noise removal. For noise detection, two extreme intensity values in an image are used to detect possible "noise pixels". For noise removal, the switching mechanism only selects "noise pixels" for processing to avoid altering any fine image details, and only the identified noise-free pixels are used to achieve better denoising performance. Two filtering techniques, the edge-preserving filtering (EPF) and the extremum-compressing median filtering (ECMF), are employed for edge-preserving and noise removal. The EPF provides higher correlation between the corrupted pixel and neighborhood pixel, which gives rise to better edge preservation. The ECMF can yield an appropriate estimation by selecting the median pixel from the noise-free pixels of current filtering window. The proposed algorithm is tested on different images and provides a better restoration performance over some of the salt and pepper noise filters. Povzetek: V prispevku je predstavljena učinkovita metoda za odstranjevanje šuma iz slik.