Parameter Estimation of Fractional Wiener Systems with the Application of Photovoltaic Cell Models

被引:2
|
作者
Zhang, Ce [1 ]
Meng, Xiangxiang [2 ]
Ji, Yan [2 ]
机构
[1] Yantai Vocat Coll, Yantai 264670, Peoples R China
[2] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao 266061, Peoples R China
基金
中国国家自然科学基金;
关键词
gradient search; forgetting factor; multi-innovation theory; fractional-order system; dynamic photovoltaic model; ESTIMATION ALGORITHMS; NONLINEAR PROCESSES; FAULT-DIAGNOSIS; IDENTIFICATION; OPTIMIZATION; GRADIENT; TRACKING; DELAY;
D O I
10.3390/math11132945
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Fractional differential equations are used to construct mathematical models and can describe the characteristics of real systems. In this paper, the parameter estimation problem of a fractional Wiener system is studied by designing linear filters which can obtain smaller tunable parameters and maintain the stability of the parameters in any case. To improve the identification performance of the stochastic gradient algorithm, this paper derives two modified stochastic gradient algorithms for the fractional nonlinear Wiener systems with colored noise. By introducing the forgetting factor, a forgetting factor stochastic gradient algorithm is deduced to improve the convergence rate. To achieve more efficient and accurate algorithms, we propose a multi-innovation forgetting factor stochastic gradient algorithm by means of the multi-innovation theory, which expands the scalar innovation into the innovation vector. To test the developed algorithms, a fractional-order dynamic photovoltaic model is employed in the simulation, and the dynamic elements of this photovoltaic model are estimated using the modified algorithms. Concurrently, a numerical example is given, and the simulation results verify the feasibility and effectiveness of the proposed procedures.
引用
收藏
页数:22
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