GENETIC ALGORITHM FOR THE REDUCTION OF REACTIVE POWER LOSSES IN RADIAL DISTRIBUTION SYTEM

• Main Author: THIN, LO THIN
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi Petronas 2006
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://utpedia.utp.edu.my/6979/1/2006%20-%20GENETIC%20ALGORITHM%20FOR%20THE%20REDUCTION%20OF%20REACTIVE%20POWER%20LOSSES%20IN%20RADIAL%20DISTRIBUTION%20SYTEM.pdf; http://utpedia.utp.edu.my/6979/

Power losses in distribution system have become the most concerned issue in power losses analysis in any power system. In the effort of reducing power losses within distribution system, reactive power compensation has become increasingly important as it affects the operational, economical and quality of service for electric power systems. Hence, the objective of the project is to perform a study and analysis on the power losses in radial distribution network by applying genetic algorithm approach for reduction of the reactive power losses. In this project, IEEE 34-bus Standard Test System is used together with the MATLAB and ERACS as powerful tools for the analysis and simulation work. Necessary literature reviews and research are conducted extensively in order to achieve the objectives of the project. The total loss saving for both single and multiple capacitor placements is 22.52% and 22.07% respectively. Single capacitor insertion is more cost effective as compare to multiple capacitor insertion because it have higher kW/kVAR ratio which is 2.696 and 2.163 respectively. Heuristic Search Strategies has total loss saving of 24.18% and 23.82% respectively for single and multiple capacitor insertions while GA has 22.52% and 22.07%). However, Genetic Algorithm is identified to be more cost effective because it has higher IcW/kVAR ratio which is 2.696 and 2.163 for single and multiple capacitor insertion respectively for while 1.9885 and 2.158 for Heuristic Search Strategies. The objective and goal towards the end of the project is to achieve the reduction of reactive power loss using genetic algorithm. The final results of the project successfully provide solutions to the reduction of reactive power losses, which eventually further contribute to the entire electrical power system in achieving superior performance in the context of operational, economical and quality of service.