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Reactive power optimization scheduling (ORPD-optimal reactive power dispatch) can optimize the reactive power flow distribution , reduce the power loss and voltage loss of grid , thus changing the voltage quality , electrical equipment can operate safely and reliably . Importance to guarantee the security and economy of modern power systems , reactive power optimization scheduling has been widespread concern. Essentially , reactive power optimization scheduling problem is a local minimum constrained global optimization problems, and contains a large number of discrete variables . Particle swarm optimization (PSO) with parallel processing , robustness , very natural , very easy to deal with mixed-integer nonlinear programming problems , find greater probability global optimal solution , and the computational efficiency higher than traditional random method . Its biggest advantage is simple and easy to implement , fast convergence , and profound intelligent background , both for scientific research , but also for engineering applications . This article describes the power system reactive power optimization of the present situation and development of research in the field , to establish the basic mathematical model of the problem of reactive power optimization , particle swarm optimization convergence and characteristics , proposed the concept is more accurate than the basic PSO neighborhood topology particle Swarm Optimization (NTPSO). In this paper, five neighborhood topology PSO on the IEEE 30-bus system and the IEEE 57 -node system reactive power optimization calculation with the basic genetic algorithm optimization results , results showed that the the Square the topological 's NTPSO get the most ideal the optimization results confirmed the superiority of the the Square topology NTPSO in solving power system reactive power optimization problem , and provide new ideas for solving large-scale power system reactive power optimization problem .
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