Articles written in Sadhana
Volume 39 Issue 6 December 2014 pp 1295-1310
Delay has a significant role to play in the implementation of the predictive current control scheme as large amount of calculations are involved. Compensating delay in the predictive current controller design can lead to an improved load current total harmonic distortion (THD) and also an increased switching frequency. Minimization of switching frequency while maintaining the lower value of load current THD requires multiple objective optimization which is achieved by optimizing a single objective function, constructed using weighting factors as a linear combination of individual objective function. The effect of weighting factor on the switching frequency minimization and the current tracking error with delay compensation for the two level voltage source inverter (VSI) are investigated in this paper. The outcomes of the predictive current control using an optimized weighting factor which is calculated using branch and bound algorithm with the delay compensation are compared with the PWM based current control scheme. The experimental tests are conducted on a 2.2 kW VSI to verify the simulation observations.
Volume 42 Issue 3 March 2017 pp 343-352
Among the numerous direct torque control techniques, the finite-state predictive torque control (FS-PTC) has emerged as a powerful alternative as it offers the fast dynamic response and the flexibility to optimize multiple objectives simultaneously. However, the implementation of FS-PTC for multiple objectives optimization requires the optimization of a single objective function, which is constructed using weighting factors asa linear combination of individual objective functions. Traditionally, the weighting factors are determined through a non-trivial process, which is a complex and time-consuming task. In an effort to avoid the timeconsuming task of weighting factor selection, this paper aims at replacing the weighting factor calculation with a systematic fuzzy multiple-criteria decision making in which the individual objective functions may have equalor varying degrees of importance. As a result the weighting factor calculation can be completely avoided. The simulation and experimental tests are conducted on a 2.2 kW induction motor drive to validate the proposed approach. The result outcomes are compared with the conventional predictive torque control (PTC) using weighting factors on the same experimental platform.
Volume 48 All articles Published: 21 December 2022 Article ID 0002
Series solution (SS) of (2+1) dimensional wave equation has been represented by Neural Network (NN) model. Experiments have been performed to find a combination of NN structure and size of sample data such that error and time cost are the minimum. Error and time cost of NN models have been studied w.r.tstructure, size of sample, random choice of initial weights and selection of data. Peak signal to noise ratio (PSNR) between series solution and solution by NN model is approximately 30 dB. This ensures that solution obtained by NN model is acceptable at human visual system. The average training time of constructed NN models is better than state-of-the-art related to solution of differential equation by NN. NN models are 18 times faster than series solution for solution prediction.