Research On Multi-objective Intelligent Method In Power Network Planning Considering Geography Factor

With the development of national economy and the improving people’s living standards, the supply capacity of distribution network, power quality and reliability requirements are increasing, the power company is facing many new challenges, one important issue is how to improve the quality and reliability of power supply at the same time, as much as possible to reduce operating, maintenance and construction costs, and achieved the unity of the reliability and economy. Therefore, how to balance the multi-object optimization problem in distribution network planning has become an urgent problem.Considering the power network planning scheme in actual construction process will often appear due to the geographical environment, and various difficult factors in selecting the corridor of transmission lines, a general analyzing of the model of multi-object optimization in distribution network planning is given at the beginning of this thesis.In allusion to the demand of the convergence and the quality of solutions and their distribution, an improved quant um-bit particle swarm optimization algorithm(IQBPSO) is presented,IQBPSO adopts the non-dominated storing relation method for solutions population and a new population diversity preserving strategy is used which is based on the turbulence pareto max-min distance. By using convergent factor K to accelerate the convergence rate of the particle who jump out the local optimal. The MATLAB simulation results show that the proposed method has better pareto optimal solutions and closer to the pareto front on distribution than by basic non-dominated sorting genetic algorithms (NSGA) and multi-objective evolutionary algorithm (MOEA)Finally, the thesis analysis the details of14-bus system and distributiong network planning of a certain area in Qinghai province, and prove the validity and feasibility of the algorithm in real netwok planning...