| As an important part of the power system,the distribution network is an important interface between the transmission grid side and the user side.With the rapid development of economy,the scope of distribution network is getting larger and larger,and the combined structure of lines is getting more and more complex,which is followed by insufficient or unreasonable distribution of system reactive power,causing problems such as increased active loss of distribution lines and lower voltage quality of nodes at the end of lines,which is contrary to the purpose of safe and stable operation of power system.The traditional optimization algorithm has good effect in solving simple distribution network problems,but when the distribution network is more complex,the traditional algorithm can not solve the problem well.When the distribution network line structure is more complex,modern artificial intelligence optimization algorithms can overcome the drawbacks of traditional algorithms to solve such complex problems.The objective function and algorithm of reactive power optimization are different.In this paper,we first choose the minimum active network loss of distribution network as the objective function for mathematical modeling,and choose Particle Swarm Optimization(PSO)as the objective algorithm.Optimization(SPSO),an improved particle swarm algorithm(S-type-Trigonometric Function Particle Swarm Optimization,STPSO)is proposed on the basis of Standard Particle Swarm Optimization(SPSO).Secondly,the inertia weights and acceleration factors of the standard particle swarm algorithm are time-varying,so that they change nonlinearly as the iterative optimization process advances,thus improving the convergence accuracy and speed of the algorithm and reducing the risk of falling into local optimal solutions at a later stage.Finally,based on the actual installation of reactive power compensation devices in domestic distribution networks at present,shunt capacitor banks are selected as reactive power compensation devices,sensitivity analysis is applied to load nodes for sensitivity calculation,and nodes requiring compensation are connected to shunt capacitor banks for reactive power compensation.In this paper,IEEE-33 node system is used as the target optimization system,and the particle swarm algorithm before improvement and the particle swarm algorithm after improvement are verified separately in this node system using MATLAB simulation platform,and then an example simulation is verified for a 10 k V distribution network in an area of Weifang city.Through the simulation verification,the results show that the active network loss reduction rate of STPSO is13.11% higher compared with the preimproved algorithm in the IEEE-33 system;in the example verification,the active network loss reduction rate is 23.17% higher.The improved algorithm outperforms the standard particle swarm algorithm in terms of both the accuracy and speed of the optimization search,and effectively reduces the active network loss of the distribution network and improves the node voltage quality by precisely controlling the number of shunt capacitor banks to be switched. |
