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Ahmad Asri Abd Samat
Universiti Teknologi MARA

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Current PI-Gain Determination for Permanent Magnet Synchronous Motor by using Particle Swarm Optimization Ahmad Asri Abd Samat; M.S Zainal; L.N Ismail; Wan Salha Saidon; A. Idzwan Tajudin
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 2: May 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i2.pp412-421

Abstract

This paper proposes the modern approach using Particle Swarm Optimization (PSO) algorithm in determining the ideal value of Proportional Integral (PI) gain for current controller of Permanent Magnet Synchronous Motor (PMSM). Controlling the torque of PMSM and optimizing the PI-gain are the main objectives of this project. The PI controller is employed to control the speed and the torque of the PMSM with the implementation of Field Oriented Control (FOC) method. This new proposed PSO technique proved that the ability in reducing the torque ripple compared to conventional heuristic method. The ideal PI-gain acquired from the PSO was included into current PI controller. From the result obtained, it shows that the viability of the PSO technique is the best to determine PI-gain for current controller.
A Comparison Study of Learning Algorithms for Estimating Fault Location Mimi Nurzilah Hashim; Muhammad Khusairi Osman; Mohammad Nizam Ibrahim; Ahmad Farid Abidin; Ahmad Asri Abd Samat
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 2: May 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i2.pp464-472

Abstract

Fault location is one of the important scheme in power system protection to locate the exact location of disturbance. Nowadays, artificial neural networks (ANNs) are being used significantly to identify exact fault location on transmission lines. Selection of suitable training algorithm is important in analysis of ANN performance. This paper presents a comparative study of various ANN training algorithm to perform fault location scheme in transmission lines. The features selected into ANN is the time of first peak changes in discrete wavelet transform (DWT) signal by using faulted current signal acted as traveling wave fault location technique. Six types commonly used backpropagation training algorithm were selected including the Levenberg-Marquardt, Bayesian Regulation, Conjugate gradient backpropagation with Powell-Beale restarts, BFGS quasi-Newton, Conjugate gradient backpropagation with Polak-Ribiere updates and Conjugate gradient backpropagation with Fletcher-Reeves updates. The proposed fault location method is tested with varying fault location, fault types, fault resistance and inception angle. The performance of each training algorithm is evaluated by goodness-of-fit (R2), mean square error (MSE) and Percentage prediction error (PPE). Simulation results show that the best of training algorithm for estimating fault location is Bayesian Regulation (R2 = 1.0, MSE = 0.034557 and PPE = 0.014%).
Co-Authors A. Idzwan Tajudin Ahmad Farid Abidin L.N Ismail M.S Zainal Mimi Nurzilah Hashim Mohammad Nizam Ibrahim Muhammad Khusairi Osman Wan Salha Saidon