摘要
针对水轮机调速系统的辨识难题,提出了1种基于超平面原型聚类的T-S模糊模型辨识方法.基于局部模糊模型线性度的重要性,推导出1种基于超平面的模糊聚类算法.该算法以优化局部模型线性度为目标,进行模糊模型前提结构辨识,能使局部模型具有良好的线性度;它应用变尺度混沌优化方法搜索最优聚类结果,避免陷入局部极小;应用最小二乘法实现模糊模型结论参数辨识.以某水电厂水轮机调速系统为对象,采用该方法建立了T-S模糊模型,并对其进行了辨识和对比试验.结果表明:建立的T-S辨识模型具有较高的辨识精度及较强的泛化能力,提出的模型辨识方法有效可行.
A new Takagi-Sugeno （T-S） fuzzy model identification method based on hyperplane prototype clustering was proposed to solve the identification difficult of hydro-turbine governing system. Considering the importance of local models linearity, a fuzzy clustering algorithm with hyperplane prototype was deduced. Aimed at optimizing linearity of local models, this algorithm provides good linearity in structure identification of premise parts of T-S fuzzy model. This algorithm adopts mutative scale chaos optimization strategy to search （LSM） is used to built for hydro-tur the best clustering results, which can avoiding local optimum. Least Square Method identify fuzzy model consequent parameters. By this method, a T-S fuzzy model was bine governing system of hydropower plant. Results of identification and comparison experiments for this model show that, the T-S fuzzy model has high identification accuracy and strong generalization ability,and the identification method is available and feasible.
出处
《动力工程》
CAS
CSCD
北大核心
2009年第4期363-368,388,共7页
Power Engineering
基金
科技部水利部公益性行业科研专项资助项目(200701008)
国家自然科学基金雅砻江联合研究基金重点资助项目(50539140)
高等学校博士学科点专项科研基金资助项目(20050487062)
关键词
水轮机
调速系统
系统辨识
T-S模糊模型
超平面
模糊聚类
混沌优化
hydro-turbine
governing system
system identification
T-S fuzzy model
hyperplane
fuzzy clustering
chaos optimization
