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This paper proposes new evolutionary multi agent based particle swarm optimization algorithms for solving optimal power flows with security constraints(line flows and bus voltages). These methods combines the multi agents in two dimensional and cubic lattice structures with particle swarm optimization (PSO) to form two new algorithms. In Cubic lattice structured multi agent based PSO(CLSMAPSO), an agent represents a particle in cubic lattice structure to PSO, and a candidate solution to the OPF problem. In Two dimensional lattice structured multi agent based PSO(TDLSMAPSO), an agent represents a particle in square lattice structure to PSO, and a candidate solution to the OPF problem. All agents live in a cubic and square lattice like environments, with agents fixed on a lattice point in the ascending order of their fitness value. In order to obtain the optimal solution, each agent in cubic and square lattice competes and cooperates with its neighbor and also a new operator called self learning operator was introduced in this paper. Making use of these agent- agent interactions, CLSMAPSO and TDLSMAPSO realizes the purpose of minimizing the objective function value. In this paper, Variable constriction factor has been considered for TDLSMAPSO and CLSMAPSO. The OPF problem has been considered with quadratic cost function, piece wise linear quadratic cost function and quadratic cost function with sinusoidal terms were considered to realize Optimal Power Flow using CLSMAPSO and TDLSMAPSO applied to IEEE 30 bus system. Simulation results show that proposed approaches gives better solution than earlier reported approaches in very quick time.

iosr journal of electrical and electronics engineering

An Improved Cubic Lattice and Two Dimensional Lattice Structured Multi Agent Based PSO Approaches for Optimal Power Flows with Security Constraints Using Different Cost Functions