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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
M.W. Mustafa
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Implimentation of Evolutionary Particle Swarm Optimization in Distributed Generation Sizing J.J. Jamian; M.W. Mustafa; H. Mokhlis; M.A. Baharudin
International Journal of Electrical and Computer Engineering (IJECE) Vol 2, No 1: February 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (482.434 KB)

Abstract

The size of Distributed Generation (DG) is very important in order to reduce the impact of installing a DG in the distribution Network. Without proper connection and sizing of DG, it will cause the power loss to increase and also might cause the voltage in the network to operate beyond the acceptable limit. Therefore, most researchers have concentrated on the optimization technique to regulate the DG’s output to compute its optimal size. In this paper, the concept of Evolutionary Particle Swarm Optimization (EPSO) method is implemented in sizing the DG units. By substituting the concept of Evolutionary Programming (EP) in some part of Particle Swarm Optimization (PSO) algorithm process, it will make the process of convergence become faster. The algorithm has been tested in 33bus distribution system with 3 units of DG that operate in PV mode. Its performance was compared with the performance when using the traditional PSO and without using any optimization method. In terms of power loss reduction and voltage profile, the EPSO can give similar performance as PSO. Moreover, the EPSO requires less number of iteration and computing time to converge. Thus, it can be said that the EPSO is superior in term of speed, while maintaining the same performance.DOI:http://dx.doi.org/10.11591/ijece.v2i1.227
Backpropagation Neural Network Modeling for Fault Location in Transmission Line 150 kV Azriyenni Azriyenni; M.W. Mustafa; D.Y. Sukma; M.E. Dame
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 2, No 1: March 2014
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1217.282 KB) | DOI: 10.52549/ijeei.v2i1.92

Abstract

In this topic research was provided about the backpropagation neural network to detect fault location in transmission line 150 kV between substation to substation. The distance relay is one of the good protective device and safety devices that often used on transmission line 150 kV. The disturbances in power system are used distance relay protection equipment in the transmission line. However, it needs more increasing large load and network systems are increasing complex. The protection system use the digital control, in order to avoid the error calculation of the distance relay impedance settings and spent time will be more efficient. Then backpropagation neural network is a computational model that uses the training process that can be used to solve the problem of work limitations of distance protection relays. The backpropagation neural network does not have limitations cause of the impedance range setting. If the output gives the wrong result, so the correct of the weights can be minimized and also the response of galat, the backpropagation neural network is expected to be closer to the correct value. In the end, backpropagation neural network modeling is expected to detect the fault location and identify operational output current circuit breaker was tripped it. The tests are performance with interconnected system 150 kV of Riau Region.
Co-Authors Azriyenni Azhari Zakri D.Y. Sukma H. Mokhlis J.J. Jamian M.A. Baharudin M.E. Dame