Title: RESEARCH ON SUBSTATION’S SITING AND CAPACITY DETERMINATION BASED ON CLOUD GENETIC ALGORITHM
ABSTRACT
Cloud genetic algorithm (CGA) uses the idea of genetic algorithms for reference; it is an optimization genetic algorithm, which uses randomicity and stable orientation of cloud model’s droplets. Compared with traditional genetic algorithms, CGA has a faster searching speed, and is not easy to fall into local optimal solution.
Substation planning is an important part of grid planning, rationality of substation’s siting has a direct effect on the economic benefits of electric power enterprises and grid’s reliability and security, as well as pros and cons of future grid’s planning. Together with model of substation’s siting and capacity determination, this paper uses genetic algorithms to programme correspondingly; its computing results indicate that the method of substation’s siting and capacity determination based on CGA has a good optimization ability to determine substation’s site and capacity, as well as convergence performance, also it can realize automatic optimization of unavailable sites that is to be elected, and is able to meet the demand for the planning of large-scale substations in the actual power grid.
KEY WORDS: GENETIC ALGORITHM; CLOUD THEORY; MODEL OF SUBSTATION’S SITING;AUTOMATIC OPTIMIZATION OF UNAVAILABLE SITES THAT IS TO BE ELECTED
