Research On UPFC Site Selection And Capacity Determination Based On K-means Clustering

Due to the non-renewable nature of fossil fuels,stable power generation methods dominated by thermal power are gradually being replaced by clean energy generation methods such as wind power and photovoltaic power.This transition poses significant challenges to the stable operation of the power system.To meet the growing demand for electricity,China has implemented large-scale regional interconnection and ultra-high voltage transmission plans.As the power system experiences an increasing number of nonlinear loads and precision electrical devices,the technical standards for power quality are gradually raised.Flexible Alternating Current Transmission System technologies have shown great effectiveness in addressing the current issues faced by power networks.Among the FACTS devices,the UPFC stands out due to its flexibility,comprehensive performance,application range,and control precision.It can appropriately control and regulate the phase angle,impedance,active power,and reactive power on power lines.Analyzing the topology and operating principles of the series and parallel sides of UPFC,and comparing and analyzing comprehensive models,decoupling models,and power injection models,the voltage power injection model on the series side is used to study the site selection and capacity determination of UPFC.The active power flow sensitivity parameters are utilized to study the UPFC site selection strategy in the IEEE 14-node system.The accuracy of this strategy needs improvement,and it is proposed to use the K-means clustering method to analyze node stability indicators and further enhance the accuracy of site selection.The installation location of the UPFC device is confirmed,and the active power flow variation caused by the installation of UPFC on the line is analyzed.The relationship between the UPFC capacity on the line and the equivalent injected voltage on the series side is established.Taking into account the investment cost,an objective function is formulated to maximize the economic benefits.Particle swarm optimization and genetic algorithms are employed to find the optimal installation capacity,and the applicability of the two algorithms in solving the objective function is compared.Simulation results show that the particle swarm algorithm has a faster optimization rate in the solving process.Furthermore,based on the principles of K-means clustering,the particle swarm algorithm is improved by keeping the same parameter settings as before.This improvement effectively improves the convergence speed in solving the objective function and provides a reference for the application of UPFC in power systems.