摘要
考虑到模糊模型建模精度对于模糊预测控制的重要性,本文提出了一种基于引力搜索超平面聚类的模糊模型辨识方法,并将其应用到水力发电机组模糊预测控制。建立了更具物理意义的超平面聚类模型,结合引力搜索,提出了基于引力搜索的超平面聚类算法(GSHPC),以此辨识模糊模型前提参数,采用正交最小二次法辨识结论参数。在此基础上,进一步提出了水力发电机组的模糊预测控制策略。将本文所提控制策略与传统PID控制以及粒子群优化PID控制进行了对比试验,结果表明基于GSHPC的辨识方法具有较高的辨识精度,本文所提水电机组预测控制方法在提高机组控制品质方面效果明显。
This paper presents a novel clustering method for fuzzy predictive control of hydraulic turbine, based on hyper-plane prototype and gravitational search to improve fuzzy modeling accuracy that is crucial to fuzzy predictive control. In this method we developed a hyper-plane prototype clustering model, using gravitational search to construct a gravitational search hyper-plane clustering algorithm （GSHPC）. This algorithm was used for identification of the premise parameters of a T-S fuzzy model, and the consequent parameters were identified with an orthogonal least square method. Then, a fuzzy predictive control strategy for hydraulic turbine was developed using the identification results. A comparison of this strategy with PID and PSO-PID shows that it is very effective in improving control quality of hydraulic turbine and the model enhanced by GSHPC achieves high identification accuracy.
出处
《水力发电学报》
EI
CSCD
北大核心
2013年第6期272-277,共6页
Journal of Hydroelectric Engineering
基金
国家自然科学基金项目(51109088)
教育部博士点基金新教师项目(20110142120020)
中央高校基本科研业务费专项资金(2013QN114)
关键词
水力机组
预测控制
模糊模型
引力搜索
超平面聚类
hydraulic turbine
predictive control
fuzzy model
gravitational search
hyper-plane clustering
