Gaussian Mutation Based Teaching-Learning Optimization for reactive power dispatch

Abstract

This paper develops the Gaussian Mutation Based Teaching-Learning Optimization (GMBTLO) to solve different reactive power dispatch problems. In GMBTLO, Gaussian random variables are instituted, which includes both the ‘Teacher phase’ as well as ‘Learner phase’ helps to improve the efficiency of searching. Simultaneously, it ensures more feasibility to obtain the global optimal. Gaussian random variables are presented in GMBTLO, where both the ‘Teacher phase’ as well as ‘Learner phase’ is considered. This advances searching competence and guarantee a great possibility of achieving global optimum without affecting the convergence speed. In this paper, GMBTLO is implemented to enhance solution quality as well as convergence speed. Developed GMBTLO is exploited for obtaining the control variables settings including terminal voltages of a generator, transformer taps, as well as output power of shunt VAR compensators (reactive) for achieving a reduced loss in real power transmission and enhanced voltage profile. GMBTLO is validated by taking IEEE 30-bus, 57-bus along with 118-bus test systems. Also, test outcomes are matched with the results already acquired from other implemented evolutionary techniques. It is concluded that the suggested GMBTLO extends a better solution.

Keywords

• Gaussian Mutation Based Teaching-Learning-Based Optimization
• Reactive power dispatch
• Real power transmission loss
• Voltage profile