Reconfiguration Of Fish Clonal Genetic Algorithm-based Distribution Network

Distribution network reconfiguration is an important method of optimizing the distribution system, which is significant to improve the quality of power supply. The Artificial Fish Swam Clonal Genetic Algorithm (AFCGA) which algorithm based on Artificial Fish Swam Algorithm (AFSA) and Clonal Genetic Algorithm (CGA) is a novel intelligent optimization algorithm, and it is used for the distribution network reconfiguration.Firstly, after analyzing a large number of algorithms used for distribution network reconstruction and the advantage and disadvantage of CGA and AFSA, Artificial fish clonal genetic algorithm (AFCGA), which fuses the two algorithms, is established. The algorithm develop the global of clonal genetic algorithm and the directivity and rapidity of artificial fish swam algorithm, so it can rapidly find the best answer at global scope.Secondly, the mathematical model of distribution network reconstruction for reducing the power loss are build in this paper. Using the method of BFS, it successful get the purpose for analyzing the tree topology of distribution network, the forward/backward sweep method for distribution network power flow calculation in different network structure is also expatiated in the paper.After solving several important problems of distribution network reconstruction, initial swarm, code strategy and conditions of convergence of AFCGA are optimized according to distribution network characteristic. IEEE33 and IEEE69 cases are respectively used to verify the algorithm feasible. At last, Visual C# 2008 is used to design interface of Distribution network reconfiguration operation, this interface is simple to operate.The simulation results indicate that AFCGA with application to distribution network reconfiguration is independent on initial condition of network, the disadvantage of slow running rate of CGA also can be avoided. Comparing with CGA, the AFCGA not only has improved convergence capability, but also has higher search efficiency and calculation rate than other methods.