Culture-Based Particle Swarm Optimization And Application In Assessment Of Wind Energy Resources

Facing in such problems as global decline of reservation of conventional energy and poor environmental conditions, many countries recognize that it is in emergency that developing a new technique to use the new energy sufficiently. Wind energy is given close attention because of its characteristic of non-polluting, high utilization and with the prospect of large-scale development and utilization. In recent years, China also attaches great importance to the development of wind energy resources. Many large-scale wind farms have been built or are in building. Therefore, the method of the wind resource assessment and wind turbine selection has important impact on the wind farm. The primary research content of this paper is the application of intelligent optimization algorithm in wind energy assessment and optimal selection of wind turbine.After analysis of Cultural Algorithm (CA) and Particle Swarm Optimization (PSO) in details, an Enhanced Culture-Based Particle Swarm Optimization algorithm (ECPSO) has been proposed. In this mixed algorithm, PSO algorithm is used in the Population Space of CA frame and the update formula of the velocity of PSO algorithm is modified. Then this paper redesigns the knowledge of Believe Space and its update method, and a strategy is proposed which makes the history knowledge guide the update of the situational knowledge. The purpose of all the modify methods in this algorithm is to remain the diversity of the population as well as having a faster convergence velocity. The experimental result indicates that this mixed algorithm can show the effect of culture frame enough and enhance the capability of PSO algorithm efficiently.Proposing the hybrid algorithm, this paper chooses Weibull distribution parameters for the study. ECPSO algorithm is applied to optimizing the two parameters of Weibull distribution. The parameters optimized by ECPSO can obtain high precision, and the wind speed distribution curve fitted by the parameters is close to the actual curve. This not onlv reflects the practicality of the algorithm, but also reminds the optimal performance of the algorithm.Finally, this paper builds the largest capacity model and the highest performance cost rate model to meet the demand of selection of wind farm wind turbine. Enhanced Culture-Based Distributed Particle Swarm Optimization Algorithm (ECDPSO) is applied to optimizing the selection optimization model of wind turbine, to determining the type and number of wind turbine for the proposed wind farm. So ECDPSO has a good performance in the practical example.