Reactive Power Optimization Of Power System Based Improved Particle Swarm Optimization

Voltage quality is one of the key performance indicates of power quality in power system. It can be directly improved by rational distribution of power flow. The reactive power flow in the power system can be improved by the control of reactive power optimization, and thus system power loss and voltage loss can be reduced. Reactive power optimization algorithm is the core of optimal reactive control. So, study on the reactive power optimization algorithm and reactive power optimization control method is very important.Nevertheless, reactive power optimization is a nonlinear, multivariable, multi-constrained programming problem, which makes the reactive power optimization complicated. In this paper, based on the characteristics of reactive power optimization, a mathematical model of reactive power optimization, including comprehensive consideration of the practical constraints and reactive power regulation means for optimization, is established. Also particle swarm optimization (PSO) has been studied, and method based on improved particle swarm optimization for reactive power is going to be taken in this paper. The algorithm not only inherits multi-point search, robustness and many other advantages from basic particle swarm algorithm, but also avoids converging too fast or easily getting into the shortcomings of the local optimal solutions at the same time.In the general problem of the reactive power optimization, where to install the reactive power compensation equipment is an important issue. In this paper, successive removing method and the pruning technique is used to seek the most reasonable position of reactive power compensation installation. Meanwhile, in this paper, according to the characteristics of power and reactive power operating experience, a multi-objective function including a comprehensive consideration of voltage satisfaction, reactive power satisfaction, system power loss and the investment costs, is also established. When the investment cost is calculated, unit investment cost and fixed installation costs are taken into account, making the model more reasonable and practical in the power system security and economic evaluation. In this paper, MATLAB language is used to program the power flow calculation in the system and power system reactive optimization based on improved particle swarm optimization. Optimization for the IEEE 14-node system proves that the improved PSO algorithm used in this paper for reactive power optimization is effective. The algorithm is simple, convergent and of high quality for optimization, and thus suitable for solving reactive power optimization problems, with some application prospect. What's more, we use the MATLAB GUI to write a platform for reactive power optimization, allowing users to operate reactive power optimization more conveniently and intuitively.