Optimum Var Resources For Maximum Loadability Enhancement Using New Optimization Algorithm

Hassan, Elia Erwani and Abdullah, Abdul Rahim and Abdul Kadir, Aida Fazliana and Ab Aziz, Nur Hakimah and Bahaman, Nazrulazhar and Mohamad Ridzuan, Mohammad Radzi and Jifri, Mohd Hanif (2018) Optimum Var Resources For Maximum Loadability Enhancement Using New Optimization Algorithm. Project Report. UTeM, Melaka, Malaysia. (Submitted)

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Abstract

Currently, a power system is operating in a stressed condition due to the increase in demand in addition to constraint in building new power plants. The economics and environmental constraints to build new power plants and transmission lines have led the system to operate very close to its stability limits. Hence, more researches are required to study the important requirements to maintain stable voltage condition and develop new techniques in order to address the voltage stability problem. As an action, most Reactive Power Planning (RPP) objective is to minimize the total power losses while satisfying the voltage stability constraints and labeled as Secured Reactive Power Planning (SCRPP). The new alternative optimization technique called Adaptive Tumbling Bacterial Foraging (ATBFO) was introduced to solve the RPP problems in the IEEE 57 bus system. The application of the developed optimization technique was extended to solve the multi objective functions involved in Secured Reactive Power Planning problems so called Multi Objective Adaptive Tumbling Bacterial Foraging (MOATBFO). The identified of singular objective functions are generalized into a multi objective function via weighted sum method that labeled as Multi objective Secured Reactive Power Planning (MOSCRPP). In order to verify the performance of the proposed technique were used for both SCRPP and MOSCRPP in the IEEE 57 bus system thus the comprehensive analyses were also conducted with others common Meta heuristic Evolutionary Programming (Meta-EP) and original Bacterial Foraging Optimization Algorithm (BFOA). From the results it shows that the proposed ATBFO optimization is able to give better overall improvement in the objective functions for single and multi SCRPP problems.

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