基于动态模糊粒神经网络算法的负荷辨识  

Load identification of algorithm based on dynamic fuzzy granular neural network

作　　者：陶永芹[1,2] 崔杜武[1] 

机构地区：[1]西安理工大学计算机科学与工程学院,西安710048 [2]西安交通大学计算机系,西安710049

出　　处：《控制与决策》2011年第4期519-523,529,共6页Control and Decision

基　　金：国家自然科学基金项目(60743009;60873035)

摘　　要：针对电力负荷的时变、变结构和非线性等特点,提出一种动态模糊粒神经网络算法.该算法采用粒计算商空间理论和模糊神经网络技术对电力负荷进行建模.将椭圆基函数和模糊ζ-完备性作为在线参数分配机制,避免了初始化选择的随机性.根据模糊规则和输入变量的重要性,对每条规则的输入变量宽度实施在线自适应调整,从而实现了负荷参数和结构同时辨识.实验结果表明了所提出方法的可行性和有效性.An algorithm based on dynamic fuzzy granular neural network is proposed for the characteristics of power load,which is time-variant,variable structure and non-linear.The quotient space theory of granular computing and fuzzy neural network technology are used in power load modeling.Elliptic basis function and fuzzy ζ-completeness are also used as a distribution mechanism of on-line parameter to avoid the randomicity of the initialization options.Online self-adapting adjustment is executed on the width of input variable according to fuzzy rules and the importance of input variables,and then the load parameters and the structure can be identified synchronously.Experimental results show the feasibility and effectiveness of this method.

关 键 词：负荷模型 参数辨识 结构辨识 粒计算 动态模糊神经网络 

分 类 号：TM714[电气工程—电力系统及自动化]