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Research On Reactive Power Optimization Algorithm Of Power System Based On Improved Genetic Algorithm

Posted on:2019-08-03Degree:MasterType:Thesis
Country:ChinaCandidate:S Y ChangFull Text:PDF
GTID:2382330596465776Subject:Power electronics and electric drive
Abstract/Summary:PDF Full Text Request
The rational distribution of reactive power in the power system is a prerequisite for improving voltage quality and reducing active power loss,which is of great significance for the safe and stable operation of the power grid.The reactive power optimization of power system is a complex nonlinear problem.Because the current various optimization calculation methods cannot perfectly solve the reactive power optimization problem with its certain limitations.Therefore,we must continue to study and explore new ways to solve the problem.Although the research on reactive power optimization based on genetic algorithm has made great progress in the in-depth discussions of domestic and foreign experts and scholars,but due to the limitations of the genetic algorithm itself,and the variety of algorithm parameters,there are still many improve space of genetic algorithms.Based on this idea,this paper mainly focuses on the application of simple genetic algorithm in reactive power optimization of power system and the improvement of simple genetic algorithm.The specific work is as follows:(1)In the aspect of reactive power optimization,this paper studies the mathematical model,select the minimum active power loss as the optimization goal,and establishes the objective function and constraint conditions.Then,the advantages and disadvantages of Newton-Raphson method and Fast-Decoupling method in power flow calculation are studied and analyzed.Fast-Decoupling method is selected as the method of power flow calculation in this paper,and its concrete steps are studied.(2)In the aspect of genetic algorithms,this paper has studied the basic principle of the algorithm,and the algorithm is combined with Fast-Decoupling method to solve the reactive power optimization problem.Then,according to the various characteristics of reactive power optimization,and the defects of simple genetic algorithm,some corresponding improvements have been made to simple genetic algorithms,including coding,initial population generation,fitness function,crossover and mutation operators.(3)In the improvement of genetic algorithm,since there are both continuous and discrete variables of reactive power optimization,the coding method chooses the combination of real numbers and integers,among them,continuous variables are encoded in real numbers and discrete variables are encoded in integers.In the generation of initial population,a further screening is made,so that the power flow calculation in the algorithm process has a better initial value.For the easy "Precocious" defects of simple genetic algorithm,the dynamic adaptation of the fitness function and the crossover and mutation operator makes the algorithm not easy to "Premature",avoid falling into local optimum,and accelerate the convergence speed of the algorithm.(4)Based on the MATLAB simulation platform,Fast-Decoupling method,simple genetic algorithm and improved genetic algorithm are applied to IEEE14 node and IEEE30 node test system respectively.Comparing and analyzing the simulation result data,simple genetic algorithm and improved genetic algorithm applied to reactive power optimization have achieved good results,effectively reduced the system active power loss,and improved the system's voltage quality.At the same time,compared with simple genetic algorithm,the improved genetic algorithm has lower active power loss,higher voltage quality and fewer iterations.It is proved that the improved genetic algorithm is more effective in reactive power optimization of power systems.
Keywords/Search Tags:Power System, Reactive power optimization, Active power loss, Genetic algorithm, Power flow calculation
PDF Full Text Request
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