Comparative Analysis of Multi Objective Optimal Power Flow in Power Systems

This paper consists of the comparative analysis of different methods applied to loss minimization in power system. The objective is to minimize the total power loss and keep the power outputs of generators; bus voltages, shunt capacitors/reactors and transformer tap setting in their specified limits. By maintaining the whole system power loss as minimum there by minimum cost allocation can be achieved. This project explains a comparative analysis between Gradient methods, Search methods and Genetic Algorithm Approach (GA). The Gradient and Search methods are the iterative local optimization methods applied to the power system till the loss minimization is realized. The GAs is the part of evolutionary algorithms family, which is computational models, inspired in the nature. GAs are powerful stochastic search algorithms based on the mechanism of natural selection in natural genetics. GAs work with a population of binary string searching many peaks in parallel. By employing genetic operators, they exchange information between peaks, hence reducing the possibility of ending at a local optimum. IEEE 30 Bus system has been studied to show the effectiveness of algorithm. Three methods are implemented to IEEE 30 bus system for minimization of active power loss and comparative analysis is made regarding computer memory and total time for computation