Genetic Algorithm based Methodology for Optimisation of innovative Switchgear Design

Life Cycle Cost (LCC) analysis of substations is an important instrument to improve substations and to improve innovative layouts. The LCC calculation methods used today provide good results for ascertained plants. However, because of the high number of possible variation the classic LCC method cannot be used as sensitivity analysis to carry out design trends. The present paper shows the application of a genetic algorithm to optimize substation life cycle cost, to determine cost sensitive component parameters and to derive design trends from the results. The Genetic Algorithm (GA) with its elements of selection, variation, crossover and mutation is described. A simulation example is given.