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HYBRID DIFFERENTIAL EVOLUTION PARTICLE SWARM

OPTIMIZATION ALGORITHM FOR REACTIVE POWER

OPTIMIZATION

ABSTRACT

The reactive power optimization problem has a significant influence on secure and

economic operation of power systems. With the regulation of the voltage level of the generators,

the taps of the transformer with OLTC and the switchable shunt capacitor/reactor groups, and so

on, the reactive power optimization must improve system voltage profiles while minimizing

system losses at all times.

Conventional optimization techniques such as nonlinear programming, linear

programming, and quadratic programming et al. have been used to solve the reactive power

optimization problem. However, because of the non differential and non-linearity nature of the

reactive power optimization problem, majority of these techniques converge to a local optimum.

In recent years, many new stochastic search methods have been developed for the global

optimization problem such as genetic algorithm, simulated annealing and many others. These

techniques search for the global or quasi-global optimum and the results reported were promising

and encouraging for further research in this direction.

Differential evolution (DE) and particle swarm optimization (PSO) are population-based

optimization algorithms. Due to their excellent convergence characteristics and few control

parameters, DE and PSO have been applied to obtain optimal solutions to some real valued

problems efficiently. DE may be trapped in local optima. Also, basic PSO has drawbacks of

prematurity, slow search speed and low convergence accuracy. Recently, many improved PSO

algorithms have been proposed by incorporating with DE, so as to explore better solutions. Some

hybrid DEPSO algorithms have been applied to reactive power optimization problem

successfully, however, so far no conclusive conclusion has been reached in which hybrid

DEPSO algorithm is better than the others.

This paper has presented and compared three algorithms based on swarm intelligence and

evolutionary techniques for solving the reactive power optimization problem. All three

algorithms are able to successfully restore the bus voltages to prescribed limits while lowering

the system active power losses. Reactive power optimization is a mixed integer nonlinear

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programming problem where metaheuristics techniques have proven suitable for providing

optimal solutions. The objective of this nonlinear optimization is minimization of system losses

and improvement of voltage profiles in a power system. A hybrid differential evolution particle

swarm optimization algorithm is presented to obtain the global optimum. To validate the

effectiveness of the algorithm, the simulation results are compared with other optimization

algorithms. It is shown that the approach developed is feasible and efficient.