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Journal : Bulletin of Electrical Engineering and Informatics

Load Management for Voltage Control Study Using Parallel Immunized-computational Intelligence Technique Amirul Izzat Abu Bakar; Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Nor Azura Md Ghani
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (646.978 KB) | DOI: 10.11591/eei.v7i2.1172

Abstract

The increase of power demand is a crucial issue in the power system community in many parts of the world. Malaysia has also witnessed the familiar scenario due to the current development throughout the country has invited the urgency of increase in the power supply. Since Malaysia practices vertical system; where the electricity is supplied by only one utility, load management is an important issue so that the delivery of electricity is implemented without discrimination. Parallel Computational Intelligence will be developed which can alleviate and avoid all the unsolved issues, highlighting the weakness of current schemes. Parallel Computational Intelligence is developed to manage the optimal load in making sure the system maintains the stability condition, within the voltage limits. This paper presents evolutionary programming (EP) technique for optimizing the voltage profile. In this study, 3 algorithms which are Gaussian, Cauchy and Parallel EP were developed to solve optimal load management problem on IEEE 26-bus Reliability Test System (RTS). Results obtained from the study revealed that the application of Parallel EP has significantly reduced the time for the optimization process to complete.
FACTS device installation in transmission system using whale optimization algorithm Irham Syafiq Shahbudin; Ismail Musirin; Saiful Izwan Suliman; Afdallyna Fathiyah Harun; Sharifah Azma Syed Mustaffa; Hadi Suyono; Nor Azura Md Ghani
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (554.938 KB) | DOI: 10.11591/eei.v8i1.1442

Abstract

As the world is progressing forward, the load demand in the power system has been continuously increasing day by day. This situation has forced the power system to operate under stress condition due to its limitation. Therefore, due to the stressed condition, the transmission losses faced higher increment with a lower minimum voltage. Theoretically, the installation of the Flexible AC Transmission System (FACTS) device can solve the problem experienced by the power system. This paper presents the whale optimization algorithm for loss minimization using FACTS devices in the transmission system. Thyristor controlled series compensator (TCSC) is chosen for this study. In this study, WOA is developed to identify the optimal sizing of FACTS device for loss minimization in the power system. IEEE 30- bus RTS was used as the test system to validate the effectiveness of the proposed algorithm.
Results of Fitted Neural Network Models on Malaysian Aggregate Dataset Nor Azura Md Ghani; Saadi Bin Ahmad Kamaruddin; Ismail Musirin; Hishamuddin Hashim
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (423.771 KB) | DOI: 10.11591/eei.v7i2.1177

Abstract

This result-based paper presents the best results of both fitted BPNN-NAR and BPNN-NARMA on MCCI Aggregate dataset with respect to different error measures.  This section discusses on the results in terms of the performance of the fitted forecasting models by each set of input lags and error lags used, the performance of the fitted forecasting models by the different hidden nodes used, the performance of the fitted forecasting models when combining both inputs and hidden nodes, the consistency of error measures used for the fitted forecasting models, as well as the overall best fitted forecasting models for Malaysian aggregate cost indices dataset.