Articles
<![CDATA[Intelligent swarm-based optimization technique for oscillatory stability assessment in power system ]]>
<![CDATA[N. A. M. Kamari]]>;
<![CDATA[I. Musirin]]>;
<![CDATA[A. A. Ibrahim]]>;
<![CDATA[S. A. Halim]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v8.i4.pp342-351
<![CDATA[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.]]>
<![CDATA[Oscillatory Stability Prediction Using PSO Based Synchronizing and Damping Torque Coefficients ]]>
<![CDATA[Nor Azwan Mohamed Kamari]]>;
<![CDATA[I. Musirin]]>;
<![CDATA[Z. A. Hamid]]>;
<![CDATA[M. H. M. Zaman]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v7i3.961
<![CDATA[This paper presents the assessment of stability domains for the angle stability condition of the power system using Particle Swarm Optimization (PSO) technique. An efficient optimization method using PSO for synchronizing torque coefficients Ks and damping torque coefficients Kd to identify the angle stability condition on multi-machine system. In order to accelerate the determination of angle stability, PSO is proposed to be implemented in this study. The application of the proposed algorithm has been justified as the most accurate with lower computation time as compared to other optimization techniques such as Evolutionary Programming (EP) and Artificial Immune System (AIS). Validation with respect to eigenvalues determination, Least Square (LS) method and minimum damping ratio ξmin confirmed that the proposed technique is feasible to solve the angle stability problems.]]>
<![CDATA[Reliability study on the adaptation of Dijkstra’s algorithm for gateway KLIA2 indoor navigation ]]>
<![CDATA[K. A. F. A. Samah]]>;
<![CDATA[A. A. Sharip]]>;
<![CDATA[I. Musirin]]>;
<![CDATA[N. Sabri]]>;
<![CDATA[M. H. Salleh]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v9i2.2081
<![CDATA[This paper describes a reliability study on the adaptation of Dijkstra’s algorithm (DA) in the indoor environment for navigation purposes. Gateway KLIA2 located at Sepang, Selangor, Malaysia has been chosen as the area for case study. Gateway KLIA2 is divided into 4 levels but this research focused on Level 2 only that consists of 129 shop lots. A survey conducted towards 68 public respondents before the adaptation and most of them responded that they are not aware of the surrounding of KLIA2 and are facing difficulties in finding the information or location of certain places inside the building. DA was chosen because it helps the users to navigate using the shortest path to destination. It is proven that through the adaptation of DA, we are able to provide the shortest distance for indoor navigation from current location to the destination location. G-INS is reliable based on the functionality and reliability testing conducted towards 15 users with the distance reduction of 47% t-test result of 0.01303 (p less than 0.05), indicates the system is accepted.]]>
<![CDATA[Oscillatory Stability Prediction Using PSO Based Synchronizing and Damping Torque Coefficients ]]>
<![CDATA[Nor Azwan Mohamed Kamari]]>;
<![CDATA[I. Musirin]]>;
<![CDATA[Z. A. Hamid]]>;
<![CDATA[M. H. M. Zaman]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
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Full PDF (651.535 KB)
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DOI: 10.11591/eei.v7i3.961
<![CDATA[This paper presents the assessment of stability domains for the angle stability condition of the power system using Particle Swarm Optimization (PSO) technique. An efficient optimization method using PSO for synchronizing torque coefficients Ks and damping torque coefficients Kd to identify the angle stability condition on multi-machine system. In order to accelerate the determination of angle stability, PSO is proposed to be implemented in this study. The application of the proposed algorithm has been justified as the most accurate with lower computation time as compared to other optimization techniques such as Evolutionary Programming (EP) and Artificial Immune System (AIS). Validation with respect to eigenvalues determination, Least Square (LS) method and minimum damping ratio ξmin confirmed that the proposed technique is feasible to solve the angle stability problems.]]>
<![CDATA[Performance comparison of distributed generation installation arrangement in transmission system for loss control ]]>
<![CDATA[S. A. S.]]>;
<![CDATA[I. Musirin]]>;
<![CDATA[S. I. Sulaiman]]>;
<![CDATA[M. H. Mansor]]>;
<![CDATA[S. A. Syed Mustaffa]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.1443
<![CDATA[Placing Distributed Generation (DG) into a power network should be planned wisely. In this paper, the comparison of having different installation arrangement of real-power DGs in transmission system for loss control is presented. Immune-brainstorm-evolutionary programme (IBSEP) was chosen as the optimization technique. It is found that optimizing fixed-size DGs locations gives the highest loss reduction percentage. Apart from that, scattered small-sized DGs throughout a network minimizes transmission loss more than allocating one biger-sized DG at a location.]]>
<![CDATA[Performance comparison of distributed generation installation arrangement in transmission system for loss control ]]>
<![CDATA[S. A. Shaaya]]>;
<![CDATA[I. Musirin]]>;
<![CDATA[S. I. Sulaiman]]>;
<![CDATA[M. H. Mansor]]>;
<![CDATA[S. A. Syed Mustaffa]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.1443
<![CDATA[Placing Distributed Generation (DG) into a power network should be planned wisely. In this paper, the comparison of having different installation arrangement of real-power DGs in transmission system for loss control is presented. Immune-brainstorm-evolutionary programme (IBSEP) was chosen as the optimization technique. It is found that optimizing fixed-size DGs locations gives the highest loss reduction percentage. Apart from that, scattered small-sized DGs throughout a network minimizes transmission loss more than allocating one biger-sized DG at a location.]]>
<![CDATA[Oscillatory Stability Prediction Using PSO Based Synchronizing and Damping Torque Coefficients ]]>
<![CDATA[Nor Azwan Mohamed Kamari]]>;
<![CDATA[I. Musirin]]>;
<![CDATA[Z. A. Hamid]]>;
<![CDATA[M. H. M. Zaman]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
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Check in Google Scholar
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Full PDF (651.535 KB)
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DOI: 10.11591/eei.v7i3.961
<![CDATA[This paper presents the assessment of stability domains for the angle stability condition of the power system using Particle Swarm Optimization (PSO) technique. An efficient optimization method using PSO for synchronizing torque coefficients Ks and damping torque coefficients Kd to identify the angle stability condition on multi-machine system. In order to accelerate the determination of angle stability, PSO is proposed to be implemented in this study. The application of the proposed algorithm has been justified as the most accurate with lower computation time as compared to other optimization techniques such as Evolutionary Programming (EP) and Artificial Immune System (AIS). Validation with respect to eigenvalues determination, Least Square (LS) method and minimum damping ratio ξmin confirmed that the proposed technique is feasible to solve the angle stability problems.]]>
<![CDATA[Performance comparison of distributed generation installation arrangement in transmission system for loss control ]]>
<![CDATA[S. A. S.]]>;
<![CDATA[I. Musirin]]>;
<![CDATA[S. I. Sulaiman]]>;
<![CDATA[M. H. Mansor]]>;
<![CDATA[S. A. Syed Mustaffa]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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Full PDF (532.635 KB)
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DOI: 10.11591/eei.v8i1.1443
<![CDATA[Placing Distributed Generation (DG) into a power network should be planned wisely. In this paper, the comparison of having different installation arrangement of real-power DGs in transmission system for loss control is presented. Immune-brainstorm-evolutionary programme (IBSEP) was chosen as the optimization technique. It is found that optimizing fixed-size DGs locations gives the highest loss reduction percentage. Apart from that, scattered small-sized DGs throughout a network minimizes transmission loss more than allocating one biger-sized DG at a location.]]>
<![CDATA[Multiple DG Planning Considering Distribution Loss and Penetration Level using EMEFA-ANN Method ]]>
<![CDATA[S. R. A. Rahim]]>;
<![CDATA[I. Musirin]]>;
<![CDATA[M. M. Othman]]>;
<![CDATA[M. H. Hussain]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 7, No 1: July 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v7.i1.pp1-8
<![CDATA[This paper presents the implementation of multiple distributed generation planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concept. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system.]]>
<![CDATA[Oscillation stability enhancement using multi-objective swarm based technique for smib system ]]>
<![CDATA[N. A. M. Kamari]]>;
<![CDATA[I. Musirin]]>;
<![CDATA[M. K. M. Zamani]]>;
<![CDATA[S. A. Halim]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v16.i2.pp631-639
<![CDATA[This paper discussed the impact of multi-objective function in a swarm-based optimization approach for modulate proportional-integral-derivative (PID) controller parameters of a static var compensator (SVC) in a single machine infinite bus (SMIB) system. SVC is a Flexible Alternating Current Transmission Systems (FACTS) devices which often used to increase the damping of the synchronous generator. In this paper, three parameters of PID controller: proportional gain, KP, interval gain, KI and derivative gain, KD are tuned with particle swarm optimization (PSO) approach. One multi-objective function (MO) that derived from the consolidation of maximum damping factor, σmax and minimum damping ratio, ξmin is proposed to elevate the damping capability of the systems. Validation with respect to speed response and eigenvalues verification proved that the proposed MO is more competent than single objective function.]]>
