Study Of Distribution Network Optimization Reconfiguration Based On Max-Min Ant Colony Algorithm

With the continuous development of the national economy, the electricity customers’requirement for a more reliable and stable distribution network has become higher. Bychanging the opening and closing state of the feeder section switch and contact switch tochange the operation of the network structure, reconfiguration improve security, reliabilityand economy of power system network.This paper establishes a distribution network optimization reconfigurationmathematical model. By analyzing the economic benefits and degree of difficulty, thispaper chose network power loss as objective function of the distribution networkoptimization reconfiguration. Selection in the initial tree network generated the associatedmatrix discrimination and to establish a network of radiation generated in the iterativeprocess. By analyzing the advantages and disadvantages of the various flow calculationmethod, this paper select forward and backward flow calculation method, and then thepaper analyze the convergence of the power flow calculation.Combined with the traditional ant colony algorithm for distribution networkoptimization reconfiguration, this paper proposes the idea of applying Max-Min antcolony algorithm to the distribution network reconfiguration by analyzing the defects oftraditional ant colony algorithm. The paper establishes new pheromone updating methodthat only elite ants are allowed to update pheromone, and then the paper limits the range ofthe pheromone by providing the upper and lower limited value, what not only greatlyreduce the possibility of the algorithm fall into a local optimum, but also improve theconvergence speed of the algorithm.For applying Max-Min ant colony algorithm to distribution network optimizationreconfiguration, we programming algorithm processes using MATLAB m-file. Inconvenient of comparison, this paper selects American PG&E69-node system as thereference system for testing. Compared with other intelligent algorithms and a variety ofimproved ant colony algorithm, the analysis results show that: there is an obvioussuperiority between the Max-Min ant colony algorithm and others algorithms inDistribution Network Optimization Reconfiguration. Finally, this paper analyzes the way that the parameter settings affect the algorithm results, reaching a reasonable parametersetting mode.