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Reactive Power Optimization Based On Hybrid Quantum-behaved Particle Swarm Optimization

Posted on:2014-02-16Degree:MasterType:Thesis
Country:ChinaCandidate:P S LiFull Text:PDF
GTID:2232330398459485Subject:Power system and its automation
Abstract/Summary:PDF Full Text Request
With the development of technology and society, the electric power industry has become the pillar industry of our country. To ensure the safe, economical and effective operation of power systems is a hot topic now. Reactive power optimization has attracted attentions from society for its close relationship to the topic.Reactive power optimization makes the assignment of reactive power reasonable, which reduces the reactive power of power transmission. Then, the transmission efficiency of active power is increased and power loss is reduced, which makes the power systems operation economical. Apart from that, reactive power optimization is effective in improving electricity and enhancing the stability and reliability of power grid.On the background, this thesis uses improvement and application of the Quantum-behaved Particle Swarm Optimization as the core to research on the technology of reactive power optimization. The main works can be summed up as follows:(1) At the present stage, the single objective function cannot meet the demands of economic and good power quality at the same time. Therefore the mathematical model with a multi-objective function is established while the mathematical model considers both the power losses and effects of the voltage deviation.(2) Quantum-behaved Particle Swarm Optimization has fast calculation speed while it is weak at global searching and easy to converge into the local optimum; SQPSO which is generated by introducing local optimum position into QPSO is effective in global searching while it works slowly. To solve these problems, the Hybrid QPSO is proposed in this thesis. The theory is to separate the whole population into two independent groups which is named as main group and assistant group. During calculation, the QPSO is employed in main group while SQPSO is employed in assistant group, then some particles are switched between two groups according to the rules. The high efficiency and high accuracy in global searching are ensured in HQPSO while its calculation speed is higher than SPQSO. (3) The HQPSO is employed in reactive power optimization while the IEEE30-bus power system is analyzed. During the analysis of IEEE30-bus power system, the influence from weight setting to results is discussed firstly. Then, the fast calculation speed and high efficiency of global searching are proved by comparison of the converge characteristics of the GQPSO. SQPSO and HQPSO. Finally, it is proved that the reactive power optimization is efficient in reducing power loss and improving power quality.(4) The reactive power optimization is applied in simplified Shandong Grid, it is validated that after optimization, the economic and power quality are both improved which proves reactive power optimization is meaningful in actual power systems.
Keywords/Search Tags:Power systems, Reactive power optimization, Multi-objective function, Hybrid quantum-behaved particle swarm optimization
PDF Full Text Request
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