Research on Automatic Generation Control with Wind Power Participation Based on Predictive Optimal 2-Degree-of-Freedom PID Strategy for Multi-area Interconnected Power System

2019

112

Summary This paper is concerned with the joint estimation of states and parameters of a special class of nonlinear systems, ie, bilinear systems. The key is to investigate new estimation methods for interactive state and parameter estimation of the considered system based on the interactive estimation theory. Because the system states are unknown, a bilinear state observer is established based on the Kalman filtering principle. Then, the unavailable states are updated by the state observer outputs recursively. Once the state estimates are obtained, the bilinear state observer–based hierarchical stochastic gradient algorithm is developed by using the gradient search. For the purpose of improving the convergence rate and the parameter estimation accuracy, a bilinear state observer–based hierarchical multi‐innovation stochastic gradient algorithm is proposed by expanding a scalar innovation to an innovation vector. The convergence analysis indicates that the parameter estimates can converge to their true values. The numerical example illustrates the effectiveness of the proposed algorithms.

Section: Discussionmentioning

confidence: 99%

“…The proposed methods in this paper can be extended to study the parameter estimation problems of different systems with complex disturbances [72][73][74][75] and can be applied to other engineering areas [76][77][78][79][80][81] such as signal modeling and communication networked systems. [82][83][84][85][86]…”

Section: Discussionmentioning

confidence: 99%

Recursive parameter estimation methods and convergence analysis for a special class of nonlinear systems

Xiao

2019

112

“…The proposed methods proposed in this paper can be used for modeling and prediction [61][62][63][64][65] and can be extended to study the parameter estimation problems of different systems with colored noises [66][67][68][69][70] and can be applied to other literatures [71][72][73][74][75] such as information and networked communication systems. [76][77][78][79][80][81][82]…”

Section: Discussionmentioning

confidence: 99%

Moving data window gradient‐based iterative algorithm of combined parameter and state estimation for bilinear systems

Liu

Hayat

2020

Summary The combined iterative parameter and state estimation problem is considered for bilinear state‐space systems with moving average noise in this paper. There are the product terms of state variables and control variables in bilinear systems, which makes it difficult for the parameter and state estimation. By designing a bilinear state estimator based on the Kalman filtering, the states are estimated using the input‐output data. Furthermore, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. A state estimator‐based MDW gradient‐based iterative (MDW‐GI) algorithm is proposed to estimate the unknown states and parameters jointly. Moreover, given the extended gradient‐based iterative (EGI) algorithm as a comparison, the MDW‐GI algorithm can reduce the impact of noise to parameter estimation and improve the parameter estimation accuracy. The numerical simulation examples validate the effectiveness of the proposed algorithm.

“…The proposed methods in this paper can be extended to study the parameter estimation problems of bilinear systems with colored noises 12,62-64 and can be applied to other engineering literatures [65][66][67][68][69][70][71][72][73] such as signal modeling and communication networked systems. [74][75][76][77][78][79] In the future work, we will extend the method to study parameter estimation of the RBF-AR model with colored noises and study the estimation methods of model orders.…”

Section: Discussionmentioning

confidence: 99%

Recursive methods for estimating the radial basis function‐based state‐dependent autoregressive model

Zhou

et al. 2020

Summary Identifying a nonlinear radial basis function‐based state‐dependent autoregressive (RBF‐AR) time series model is the basis for solving the corresponding prediction and control problems. This paper studies some recursive parameter estimation algorithms for the RBF‐AR model. Considering the difficulty of the nonlinear optimal problem arising in estimating the RBF‐AR model, an overall forgetting gradient algorithm is deduced based on the negative gradient search. A numerical method with a forgetting factor is provided to solve the problem of determining the optimal convergence factor. In order to improve the parameter estimation accuracy, the multi‐innovation identification theory is applied to develop an overall multi‐innovation forgetting gradient (O‐MIFG) algorithm. The simulation results indicate that the estimation model based on the O‐MIFG algorithm can capture the dynamics of the RBF‐AR model very well.