中国电机工程学报2016，Vol.36Issue(23)：6327-6336,10.DOI:10.13334/j.0258-8013.pcsee.152794
风电场有功功率多目标分层递阶预测控制策略
Stratified Progressive Predictive Control Strategy for Multi-objective Dispatching Active Power in Wind Farm
摘要
Abstract
Wind farm active power control strategy needs multi-objective closed-loop coordination optimization ability for improving active power control accuracy, stability and reducing wind turbines mechanical loss. The control strategy proposed in this paper is divided into three layers, which are wind farm optimized distribution layer, flocked control layer and single turbine active power managing layer, according to the targets of improving the system active power dispatching values tracking accuracy, reducing wind turbine dispatching instructions fluctuations and wind turbine generation state optimizing. The ultra-short term wind power forecasting combined model is built in wind farm optimized distribution layer by using online sequential extreme learning machine and least squares support vector machine. The power forecasting values and system active power dispatching values are used for each wind turbine group active power load, which is delivered to flocked control layer. The active power generations of wind turbine groups are further rolling optimized based on dispatching values and wind turbine generation condition. The pertinency of power control will be improved and wind turbine dispatching changing range and frequency are reduced. Moreover, the wind power combined model forecasting errors are adjusted by real time active power values. Compared with the common used active power dispatching algorithms, this control strategy can improve the power control accuracy, reduce the wind turbine dispatching frequency and enhance active power robustness in wind farms.关键词
风电场/有功功率分配/模型预测控制/风机分群Key words
wind farm/active power dispatching/model predictive control/wind turbine grouping分类
信息技术与安全科学引用本文复制引用
叶林,任成,李智,么艳香,赵永宁..风电场有功功率多目标分层递阶预测控制策略[J].中国电机工程学报,2016,36(23):6327-6336,10.基金项目
国家自然科学基金项目(51477174，51077126)。Project Supported by National Natural Science Foundation of China (51477174,51077126) （51477174，51077126）
