Application Of Improved Particle Swarm Optimization In Reactive Power Optimization Of Power System

With the rapid development of China's economy,the coverage of the power network is expanding,the structure of the power network is becoming more and more complex,and the voltage level of the main power network is getting higher and higher.As a result,the electricity consumption of residents is also increasing,and users have higher and higher requirements on the reliability of power supply.Under the premise of the gradual marketization of the power industry,how to further optimize the resource allocation scientifically,reduce the technical network loss to the maximum extent,and improve the operation performance and economic benefits of the system while ensuring the safe and stable operation of the power grid has become an urgent issue for the power sector.The reactive power optimization problem of power system is a nonlinear combinatorial optimization problem with multiple objectives,multiple constraints and mixed variables.Although the traditional reactive power optimization method has good advantages in operation speed and convergence performance,it needs to meet some prerequisites,such as requiring the objective function to be differentiable and the control variable to be continuous.In recent years,the development and application of artificial intelligence algorithms have provided new ideas for solving reactive power optimization problems,among which particle swarm optimization(PSO)in intelligent optimization algorithm is particularly prominent in optimization problems due to its good global random search capability.Particle swarm optimization(PSO)algorithm is robust,easy to implement and efficient,but it may fall into the local optimal solution.In this paper,particle swarm optimization is improved to improve the convergence speed and convergence precision of the algorithm,and the improved particle swarm optimization is applied to the reactive power optimization problem of power system,providing a new idea for solving the problem of reactive power optimization.Firstly,the background of reactive power optimization problem is introduced,and then the purpose and significance of studying reactive power optimization problem are introduced.The principle of reactive power optimization problem is introduced by simply introducing and analyzing the existing typical algorithms for reactive power optimization.Secondly,on the basis of introducing the concept and principle of the particle swarm algorithm,the main components of the particle swarm algorithm are analyzed in detail,referring to the relevant literature,this paper makes targeted improvements to the particle swarm algorithm.By improving both the inertial weight and the acceleration factor,and selecting 4 sets of typical functions,the improved algorithm is verified and the corresponding optimization results are analyzed.The results show that the improved particle swarm algorithm overcomes the algorithm's tendency to fall into the local optimal problem,and is improving At the same time of the global optimization ability,the convergence speed and accuracy of the algorithm have also been improved.The solution idea of the improved particle swarm optimization in reactive power optimization is analyzed again,and the selection of discrete variables,fitness function,convergence criterion and power flow calculation method in the optimization process is also analyzed.Finally,the program of solving reactive power optimization problem with corresponding improved particle swarm optimization algorithm based on MATLAB language is programmed and applied to solving reactive power optimization problem of typical IEEE 30-node system and hanzhong power grid system.Simulation results show that the improved algorithm is effective in improving the node voltage distribution and reducing the active power network loss,which verifies the feasibility of the improved algorithm.