Analytic Hierarchy Process And Improved Genctic Algorithm For Electric Power Equipment Maintenance Schedule Optimization

As the continuous development of power systems and gradual expansion of grid size, its requirements for security, stability and reliability are increasing. Thus it's necessary to develop a reasonable equipment maintenance schedule during device's normal operating, and to make a periodic preventive equipment maintenance arrangement.Power equipment maintenance schedule optimization is a large-scale combinatorial optimization problem. This paper contrapose the difficulty of weight coefficient of different optimization goal to quantitative analysis, introduce Analytic Hierarchy Process (AHP) to establish a clear hierarchy structure and decompose the optimization goals, and then use a comparison judgment matrix to quantify the human judgments and determine the weight coefficient, thus could reflect the relative importance of each goal accurately and objectively. In this paper, a method of using improved Genetic Algorithm for power equipment maintenance schedule optimization is proposed. By introducing several effective measures, including a special set of initial population, the optimal solution retention mechanism. using SUS to generate parent population, adaptive crossover and mutation coefficient, and optimal number of individual mutation, the improved GA could overcome the shortages of standard GA, such as too many iteration times, the premature and stagnation phenomenon in search process, and the instability of optimization results.In this paper, a monthly device maintenance schedule of power supply company in southern China is optimized, the simulation results show the rationality of the new model, and the effectiveness of the proposed algorithm.