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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
I. Musirin
Universiti Teknologi Mara
Computer Science & IT Electrical & Electronics Engineering Engineering

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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.
PSS Based Angle Stability Improvement Using Whale Optimization Approach N. A. M. Kamari; I. Musirin; Z. Othman; S. A. Halim
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 2: November 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i2.pp382-390

Abstract

This paper introduced a new swarm based optimization technique for tuning Power System Stabilizer (PSS) that attached to a synchronous generator in a single machine infinite bus (SMIB) system. PSS which is installed with Lead-Lag (LL) controller is introduced to elevate the damping capability of the generator in the low frequency mode. For tuning PSS-LL parameters, a new technique called Whale Optimization Algorithm (WOA) is proposed. This method mimics the social behavior of humpback whales which is characterized by their bubble-net hunting strategy in order to enhance the quality of the solution. Based on eigenvalues and damping ratio results, it is confirmed that the proposed technique is more efficient than Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) in improving the angle stability of the system. Comparison between WOA, PSO and EP optimization techniques showed that the proposed computation approach give better solution and faster computation time.
Application of Immune Log-Normal Evolutionary Programming in Distributed Generation Installation M. H. Mansor; I. Musirin; M. M. Othman; S. A. Shaaya; S. A. Syed Mustaffa
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 3: June 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i3.pp730-736

Abstract

Nowadays, the location and sizing of distributed generation (DG) units in power system network are crucial to be at optimal as it will affect the power system operation in terms of stability and security. In this paper, a new technique termed as Immune Log-Normal Evolutionary Programming (ILNEP) is applied to find the optimal location and size of distributed generation units in power system network. Voltage stability is considered in solving this problem. The proposed technique has been tested on the IEEE 26 bus Reliability Test System to find the optimal location and size of distributed generation in transmission network. In order to study the performance of ILNEP technique in solving DG Installation problem, the results produced by ILNEP were compared with other meta-heuristic techniques like evolutionary programming (EP) and artificial immune system (AIS). It is found that the proposed technique gives better solution in term of lower total system loss compared to the other two techniques.
Optimal Tuning of SVC-PI Controller using Whale Optimization Algorithm for Angle Stability Improvement N. A. M. Kamari; I. Musirin; Z. A. Hamid; A. A. Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp620-624

Abstract

This paper proposed a new swarm-based optimization technique for tuning conventional proportional-integral (PI) controller parameters of a static var compensator (SVC) which controls a synchronous generator in a single machine infinite bus (SMIB) system. As one of the Flexible Alternating Current Transmission Systems (FACTS) devices, SVC is designed and implemented to improve the damping of a synchronous generator. In this study, two parameters of PI controller namely proportional gain, KP and integral gain, KI are tuned with a new optimization method called Whale Optimization Algorithm (WOA). This technique mimics the social behavior of humpback whales which is characterized by their bubble-net hunting strategy in order to enhance the quality of the solution. Validation with respect to damping ratio and eigenvalues determination confirmed that the proposed technique is more efficient than Evolutionary Programming (EP) and Artificial Immune System (AIS) in improving the angle stability of the system. Comparison between WOA, EP and AIS optimization techniques showed that the proposed computation approach gives better solution and faster computation time.
Co-Authors A. A. Ibrahim A. A. Ibrahim A. A. Sharip K. A. F. A. Samah M. A. Zulkifley M. H. Hussain M. H. M. Zaman M. H. Mansor M. H. Salleh M. K. M. Zamani M. M. Othman N. A. M. Kamari N. A. M. Kamari N. F. Ramli N. Sabri Nor Azwan Mohamed Kamari S. A. Halim S. A. Halim S. A. S. S. A. Shaaya S. A. Syed Mustaffa S. I. Sulaiman S. R. A. Rahim Z. A. Hamid Z. Othman