Font Size: a A A

Reactive Power Optimization Of Power System Based On Improved Particle Swarm Optimization Algorithm

Posted on:2018-05-08Degree:MasterType:Thesis
Country:ChinaCandidate:S M QueFull Text:PDF
GTID:2348330536979671Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
Reactive power optimization is a complex nonlinear optimization problem,which holds a large number of local minimum,multi discontinuous variables and constraints.On the premise of meeting all constraints,the reactive power optimization improves the quality of voltage,reduces the network loss of system operation and ensures stability of the system voltage,which are got by the existing optimization method to adjust controlled variables reasonable and utilize grid equipment resources.The traditional optimization methods have many shortcomings,and the optimization results are not good.With the development of artificial intelligence algorithm,more intelligent algorithms are used to solve the reactive power optimization problem than before.With the advantages of simple structure and fast convergence,the particle swarm optimization algorithm is widely applied to reactive power optimization of power system.However,the particle swarm algorithm remains to be further research and improvement,which is easy to fall into local optimum and slow of the later convergent speed.In this paper,two aspects works are researched,which are the improvement of the particle swarm optimization and the optimized power system reactive power problems by the improved particle swarm optimization algorithm.Because of falling into local optimum easily and slow convergence problems in the particle swarm algorithm optimization process later stage,the improved algorithms are designed to further improve the search ability of particle swarm optimization algorithm,which are the adaptive quantum particle swarm optimization and bacterial foraging particle swarm algorithm respectively.The adaptive quantum particle swarm algorithm introduces adaptive weights to reevaluate the ?potential well characteristics length L(t)of particle state wave function,which improves adaptive regulate ability of particles,helps population escape from the local optimum,and finds the global optimal value as soon as possible.Bacterial foraging particle swarm algorithm is a new algorithm combining the strong local search ability of the bacterial foraging algorithm and global searching ability of the particle swarm algorithm.The new algorithm holds a fast search ability of particle swarm algorithm to determine the optimal target at incipient stage and strengthens the local search latter to improve the accuracy of optimization like bacterial foraging algorithm.The superiority of the two improved algorithms compared with the standard particle swarm optimization algorithm is analyzed by simulation experiment.Considering the safety and economy of the system,a mathematical model of reactive power optimization is proposed.It regards the reducing the active power loss as a means,voltage and reactive power generator cross-border as a constraint condition.It achieves the economic operation of electric power system at the end.In this paper,the new improved particle swarm optimization algorithm is used to solve the reactive power optimization problem by testing in the IEEE30 node test system.The simulation results show that the improved algorithm has a good effect in reactive power optimization.
Keywords/Search Tags:Reactive power optimization, power flow calculation, particle swarm optimization, aggregation, quantum behaved particle swarm optimization, bacterial foraging algorithm
PDF Full Text Request
Related items