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Particle Swarm Optimization And Its Applied Research In Electric Power System

Posted on:2009-07-13Degree:DoctorType:Dissertation
Country:ChinaCandidate:J J LiFull Text:PDF
GTID:1118360302962171Subject:Power electronics and electric drive
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With the development of the power system and power market reformation, it is more and more important to assure the safety, economy, stabilization, and reliability of the power system. With the emergence of new nature and requests, there are different kinds of optimization problems in field of power system. It is, therefore, necessary to develop practical algorithm according to the characteristic of modern power system. Particle swarm optimization algorithm is a sort of rising intelligent algorithm. Its concept is simple and it is easy to be implemented. After being presented by Kennedy and Eberhart in 1995, it has achieved great development in several years and has been successfully applied to some fields. Particle swarm optimization algorithm has a strong ability to achieve the most optimistic result. Meanwhile it has a disadvantage so far as its local minimum is concerned. In this dissertation, particle swarm optimization algorithm and its application research on power system are mainly discussed. The major innovations in this article are as follows:Research on particle swarm optimization particle trajectoriesThe sufficient condition for the convergence of standard particle swarm optimization algorithm's particle trajectories, which is studied based on the spectral radius of algorithm's iteration matrix, is proposed and proved. And a kind of convenient contour map discriminance is put forward. This discriminance can be used to judge if the algorithm is convergent, measure the convergence rate. Dynamic movement analyzes and examples validate the sufficient condition for the convergence of particle trajectories.Research on modification of particle swarm optimizationThe particle swarm optimization is studied deeply and three kinds of modified PSO are put forward:A kind of gradient particle swarm optimization is presented. This method can enhance the convergence rate by tuning the inertia weight based on analyzing the gradient information in iteration process. When the optimum information of the swarm is stagnant, the global best is mutated in its minus gradient direction to change the searching direction of the swarm and reduce the possibility of trapping in local optimum. Particle trajectories of this algorithm are studied based on the sufficient condition for the convergence of particle trajectories in standard particle swarm optimization algorithm. Simulation results verify the correctness and efficiency of this method.A sort of elitist annealing particle swarm optimization algorithm is proposed. Part of the particle swarm is annealed in this algorithm. A new kind of annealing method based on the character of the Logistic function is designed. Then the currently best space is searched more detailedly. This algorithm can enhance the exploration and exploitation ability of the algorithm; at the same time the computation time is well controlled. Typical function optimization problem results show that this method possesses good convergent performance with faster convergent rate.The standard particle swarm optimization algorithm is limited in the continuous problem. A discretization process is added and a universal particle swarm optimization algorithm is put forward. First, a kind of section-set discrete method is proposed; then, a kind of greed discrete method (including Simple greed method and probability greed method) is presented. And the influence of the parameter on the discretization process is analyzed. Simulation tests indicate that proposed discrete method is efficient. And the probability greed method with appropriate parameter, which can solve the problem of local minimum of the particle swarm optimization algorithm effectively, possesses strong convergence capability.Application research on power system of particle swarm optimizationUnit commitment problem is a typical mixed combinatorial optimization problem. Various constraints are considered and analyzed. First the capacity constraints of units are modified. Then a necessary and sufficient condition is developed to determine whether the system demand constraints and the spinning reserve requirements can be satisfied by adjusting generation levels. At last the feasibility criterion of the commitment states is proposed. The gradient particle swarm optimization algorithm is applied to the unit commitment problem. A new kind of coding, pseudo active power coding, in which the up/down variable and the active power variable are fused into one variable, is proposed. Then the time complexity of computing is reduced effectively. The units are classified according to their states in every period. And the states of the units are adjusted dynamically in different periods. Then the unit commitment is optimized in all periods directly. Mathematical methods and repair methods are used to deal with the constraints. Then the quality of the swarm is improved. The results of numerical simulation demonstrate that this algorithm is efficient and practical.A kind of integrated fault restoration model of the shipboard power system is put forward, in which the service restoration of all loads and the number of switches are considered. The elitist annealing particle swarm optimization algorithm fused with the proposed discrete method is applied to the fault reconfiguration of the shipboard power system. The lost, normal, and spare power supply states of the load are coded into 0, 1, and 2. The constraints are processed by the penalty function method. Shipboard power system fault restoration tests show that better service restoration can be provided by this method, which possesses strong convergence capability.
Keywords/Search Tags:particle swarm optimization algorithm, convergence, discretization, gradient, elitist annealing, unit commitment, fault reconfiguration
PDF Full Text Request
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