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.
Highlights
Evolutionary algorithms since based on the concept of natural evolutionary processes
Results obtained from the study revealed that the application of Parallel evolutionary programming (EP) has significantly reduced the time for the optimization process to complete
The management problem will be tested on IEEE 26-bus Reliability Test System (RTS) and the detail of the test system can be referred in [22]
Summary
Evolutionary algorithms since based on the concept of natural evolutionary processes. In recent years a vast number of parallel computer architectures have emerged and are present in every desktop, notebook PC and mobile phones [3]. With these resources readily available, it has become more critical than ever to design algorithms that can be efficiently implemented in a parallel architecture and as such Evolutionary algorithms can be parallelized. This project presents the load management for voltage control study by using parallel immunizedcomputational intelligence technique. The load management has been achieved by implementing the system in an Journal homepage: http://journal.portalgaruda.org/index.php/EEI/index
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