VOLTAGE STABILITY MARGIN ENHANCEMENT THROUGH OPTIMAL LOCATION OF VAR COMPENSATOR
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Apr 13, 2009
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It is well known that voltage stability enhancement margin is interrelated with reactive power loss. To minimize the reactive power loss, location and placement of reactive power improvement devices is a major task. This paper introduces a PSO based method to find the location of series capacitors to be implanted to minimize the total reactive power loss in a power system network. The established indicator is used for the voltage stability assessment and also to find the change in reactive power loss. So obtained the series compensation requirement with the degree of compensation Ks, the voltage stability assessment is carried out on an IEEE-14 and IEEE-30 bus system. The Evolutionary computing algorithms are applied to minimize the series reactive power loss with the optimal degree of compensation. The stability assessment of the system also was carried out with Ks using PSAT. The Particle Swarm optimization results are compared with the solution obtained by the Genetic Algorithm(GA) .The comparison shows that the proposed Particle swarm optimization(PSO) technique of series reactive power loss minimization with the optimal location of series capacitors is more efficient and effective.
It is well known that voltage stability enhancement margin is interrelated with reactive power loss. To minimize the reactive power loss, location and placement of reactive power improvement devices is a major task. This paper introduces a PSO based method to find the location of series capacitors to be implanted to minimize the total reactive power loss in a power system network. The established indicator is used for the voltage stability assessment and also to find the change in reactive power loss. So obtained the series compensation requirement with the degree of compensation Ks, the voltage stability assessment is carried out on an IEEE-14 and IEEE-30 bus system. The Evolutionary computing algorithms are applied to minimize the series reactive power loss with the optimal degree of compensation. The stability assessment of the system also was carried out with Ks using PSAT. The Particle Swarm optimization results are compared with the solution obtained by the Genetic Algorithm(GA) .The comparison shows that the proposed Particle swarm optimization(PSO) technique of series reactive power loss minimization with the optimal location of series capacitors is more efficient and effective.