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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Mohamed Zellagui
Université du Québec
Computer Science & IT Engineering

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Multi-verse optimization based evolutionary programming technique for power scheduling in loss minimization scheme Muhamad Hazim Lokman; Ismail Musirin; Saiful Izwan Suliman; Hadi Suyono; Rini Nur Hasanah; Sharifah Azma Syed Mustafa; Mohamed Zellagui
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (562.209 KB) | DOI: 10.11591/ijai.v8.i3.pp292-298

Abstract

The growth of computational intelligence technology has witnessed its application in numerous fields. Power system study is not left behind as far as computational intelligence trend is concerned. In power system community, optimization process is one of the crucial efforts for most remedial action to maintain the power system security. Basically, power scheduling refers to prior to fact action (such as scheduling generators to generate certain powers for next week). Power scheduling process is one of the most important routines in power systems. Scheduling of generators in a power transmission system is an important scheme; especially its offline studies to identify the security status of the system. This determines the cost effectiveness in power system planning. This paper investigates the performance of multi-verse based evolutionary programming (lowest EP) technique in the application of power system scheduling to ensure loss is gained by the system. Losses in the system can be controlled through this implementation which can be realized through the validation on a chosen reliability test system as the main model. Validation on IEEE 30-Bus Reliability Test System resulted that both techniques are reliable and robust in addressing this issue.
Intelligent based technique for under voltage load shedding in power transmission systems Saiful Firdaus Abd Shukor; Ismail Musirin; Zulkifli Abd Hamid; Mohamad Khairuzzaman Mohamad Zamani; Mohamed Zellagui; Hadi Suyono
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 1: January 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i1.pp110-117

Abstract

The increasing demand of electric power energy and the presence of disturbances can be identified as the factors of voltage instability condition in a power system. A secure and reliable power system should be considered to ensure smooth delivery of electricity to the consumers. A power system may experience undesired event such as voltage instability condition leading to voltage collapse or cascading collapse if the system experiences lack of reactive power support. Thus, to avoid blackout and cascaded tripping, load shedding is the last resort to prevent a total damage. Under Voltage Load Shedding (UVLS) scheme is one of the possible methods which can be conducted by thepower system operators to avoid the occurrence of voltage instability condition. This paper presents the intelligent based technique for under voltage load shedding in power transmission systems. In this study, a computational based technique is developed in solving problem related to UVLS. The integration between a known computational intelligence-based technique termed as Evolutionary Programming (EP) with the under-voltage load shedding algorithm has been able to maintain the system operated within the acceptable voltage limit. Loss and minimum voltage control as the objective function implemented on the IEEE 30-Bus Reliability Test System (RTS) managed to optimally identify the optimal location and sizing for the load shedding scheme. Results from the studies, clearly indicate the feasibility of EP for load shedding scheme in loss and minimum voltage control in power system.
Co-Authors Ismail Musirin Ismail Musirin Mohamad Khairuzzaman Mohamad Zamani Muhamad Hazim Lokman Rini Nur Hasanah Saiful Firdaus Abd Shukor Saiful Izwan Suliman Sharifah Azma Syed Mustafa Suyono, Hadi Zulkifli Abd Hamid