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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
N. A. M. Kamari
Universiti Kebangsaan Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Intelligent swarm-based optimization technique for oscillatory stability assessment in power system N. A. M. Kamari; I. Musirin; A. A. Ibrahim; S. A. Halim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (721.848 KB) | DOI: 10.11591/ijai.v8.i4.pp342-351

Abstract

This paper discussed the prediction of oscillatory stability condition of the power system using a particle swarm optimization (PSO) technique. Indicators namely synchronizing (Ks) and damping (Kd) torque coefficients is appointed to justify the angle stability condition in a multi-machine system. PSO is proposed and implemented to accelerate the determination of angle stability. The proposed algorithm has been confirmed to be more accurate with lower computation time compared with evolutionary programming (EP) technique. This result also supported with other indicators such as eigenvalues determination, damping ratio and least squares method. As a result, proposed technique is achievable to determine the oscillatory stability problems.
An optimal placement of phasor measurement unit using new sensitivity indices K. Khalid; A. A. Ibrahim; N. A. M. Kamari; M. H. M. Zaman
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2499

Abstract

This paper presents an alternative approach to solve an optimal phasor measurement unit (PMU) placement by introducing two new sensitivity indices. The indices are constructed from the information measured from PMUs such as voltage magnitude and angle. These two parameters are essential for power system stability assessment and control. Therefore, fault analysis is carried out to obtain the voltage magnitude and angle deviations at all buses in order to construct the indices. An exhaustive search method is used to solve the linear integer programming problem where all possible combinations of PMU placement are evaluated to obtain the optimal solution. Unfortunately, the traditional objective function to minimize the total number of PMU placement leads to multiple solutions. The indices can be used to assess multiple solutions of PMU placement from the exhaustive method. In this work, an optimal solution is selected based on the performance of PMU placement in according to the indices. The proposed method is tested on the IEEE 14 bus test system. Only four PMUs are required to monitor the whole test system. However, the number of PMUs can be reduced to three PMUs after applying zero injection bus elimination.
Optimal power scheduling for economic dispatch using moth flame optimizer N. A. M. Kamari; M. A. Zulkifley; N. F. Ramli; I. Musirin
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp379-384

Abstract

This paper proposes the optimal generator allocation to solve economic dispatch (ED) problem in power system using moth flame optimizer (MFO). With this approach, the optimum power for each unit generating in the system will be searched based on the power constraints per unit and the amount of power demand. The objective function of this study is to minimize the total cost of generation. The amount of power loss is measured to determine the effectiveness of the proposed technique. The performance of the MFO technique is also compared to the evolutionary programming (EP) and particle swarm optimization (PSO) methods. Five- and thirty-bus power system networks are selected as test systems and simulated using MATLAB. Based on simulation results, MFO provides better results in regulating the optimum power generation with minimum generation cost and power loss, compared to EP and PSO.
Co-Authors A. A. Ibrahim I. Musirin K. Khalid M. A. Zulkifley M. H. M. Zaman N. F. Ramli S. A. Halim