电力系统自动化2016，Vol.40Issue(22)：7-14,8.DOI:10.7500/AEPS20160426008
基于数据驱动方法的风电机组功率优化
Power Optimization of Wind Turbine Generators Based on Data-driven Approach
摘要
Abstract
To improve the wind energy production and profit,it is imperative to optimize the power output of wind turbine generator (WTG).This paper extracts the analytical relation between WTG power,wind profile and control variables from historical operation data with a designed feed-forward neural network.Based on such a relation,point-to-point and cluster optimization strategies are developed and used for WTG power optimization,which optimize WTG control variables for maximum WTG power under the wind profile measured.The K-means clustering algorithm is used in the latter strategy to reduce optimization complexity,thus facilitating real-time WTG power optimization.Three new indices of mean power gain (MPG),rate of power gain (RPG) and probability of power gain (PPG) are proposed to quantify the power gains by the optimization strategies proposed.Extensive comparisons are conducted between two proposed strategies and recorded operation data using H56-850 WTG.Results show that both strategies can optimize WTG power output.In addition,cluster strategy with five clustering centers could considerably reduce optimization complexity while achieving similar effectiveness as that of the point-to-point strategy.关键词
风电机组/风电/神经网络/功率优化Key words
wind turbine generator/wind power/neural network/power optimization引用本文复制引用
缪书唯,谢开贵,杨贺钧,王蔓莉,胡博,王曼..基于数据驱动方法的风电机组功率优化[J].电力系统自动化,2016,40(22):7-14,8.基金项目
国家自然科学基金资助项目(51307185) （51307185）
国家电网公司科技项目(SGCQDKOODJJS1500056)。 （SGCQDKOODJJS1500056）
