Study On The Application Of Genetic Algorithms To Distribution Network Reconfiguration

Network reconfiguration is a nonlinear and very troublesome optimization problem, and the computational intelligence method is possibly the most ideal way to solve the problem. The dissertation has successfully searched the best structure of the distribution system by the improved genetic algorithms. The dissertation first elaborates on the computational intelligence method and its current situation in the field of power system and distribution network reconfiguration. Then it analyses genetic algorithms theoretically, and elaborates on the basic contents and the working principles of the genetic algorithms, and makes a global convergence analysis based on the Markov Chains. Based on the single genetic algorithms and the features of the distribution network reconfiguration, this dissertation makes a further study on such aspects as selection operator, crossover operator, mutation operator, termination conditions and etc, thus, puts forward improved genetic algorithms. The dissertation makes certain study on the optimization reconfiguration model of distribution network. It puts forward a multi-objective model and according to the theory of variable weight coefficients transforms the multi-objective problem into a single-objective one. It applies the IGAs to the solving of the distribution network optimization reconfiguration, making a further probe into such areas as chromosome encoding, initialization, constraints handling, construction of fitness function and the gene operation. As the optimization results show, the improved genetic algorithms has fine ability of global searching and good solution speed. The dissertation makes a basic exploration in the application of IGAs for the service restoration through network reconfiguration for distribution network. It explores its mathematical model, studys the chromosome encoding plan and constructs the fitness function. It also explores into its basic procedures in detail. As the example imitation results show, IGAs can be applied to the solving of the distribution network service restoration, and has a better convergence and a higher computation efficiency, therefore, extending prospects in the application. Such an analysis indicates that IGAs has a successful application to the distribution network reconfiguration. IGAs bears a better convergence and a higher efficiency than SGA.