Urban power network substation optimal planning based on geographic culture algorithm

Algorithm (GCA), is presented to handle optimal urban power planning about substation locating and sizing. Culture algorithm consists of population space and belief space. The cultural algorithm is different with other integer optimization algorithm, since it is systematic, guidance, population space and belief space promote mutually by communication. GCA adopts the differential evolution algorithm (DE) as population space and proposes four kinds of strategies to constitute the belief space according to the urban power network characteristic. GCA is tested by a realistic planning project and compared with particle swarm optimization (PSO) to verify the effectiveness and feasibility.