Research on the Predictive Optimal PID Plus Second Order Derivative Method for AGC of Power System with High Penetration of Photovoltaic and Wind Power

2020

132

Summary This paper is concerned with the design of a state filter for a time‐delay state‐space system with unknown parameters from noisy observation information. The key is to investigate new identification algorithms for interactive state and parameter estimation of the considered system. Firstly, an observability canonical state‐space model is derived from the original model by linear transformation for the purpose of simplifying the model structure. Secondly, a direct state filter is formulated by minimizing the state estimation error covariance matrix on the basis of the Kalman filtering principle. Thirdly, once the unknown states are estimated, a state filter–based recursive least squares algorithm is proposed for parameter estimation using the least squares principle. Then, a state filter–based hierarchical least squares algorithm is derived by decomposing the original system into several subsystems for improving the computational efficiency. Finally, the numerical examples illustrate the effectiveness and robustness of the proposed algorithms.

Section: Discussionmentioning

confidence: 99%

“…The proposed state filter-based recursive least squares algorithm for a general dynamical system with unknown states can combine other techniques and strategies [61][62][63][64] to explore new state and parameter identification methods and can be applied to other fields such as information processing and communication. [65][66][67][68][69]…”

Section: Discussionmentioning

confidence: 99%

Adaptive parameter estimation for a general dynamical system with unknown states

Xiao

2020

132

“…If the innovation length p = 1, the BSO-HMISG algorithm in (68)-(82) reduces to the BSO-HSG algorithm. If we introduce a forgetting factor in (70) and (72),…”

Section: The Hmisg Algorithmmentioning

confidence: 99%

“…The proposed state and parameter estimation algorithms for bilinear systems can combine other estimation algorithms 45,46 and the mathematical tools [47][48][49][50][51] and strategies [52][53][54][55][56] to explore new identification methods of other linear, bilinear, and nonlinear systems with colored noises [57][58][59][60][61] and can be applied to other fields such as information processing [62][63][64][65][66] and communication. [67][68][69][70][71] Remark 6. To improve the convergence rate and tracking performance of the HSG and HMISG algorithms, we introduce a forgetting factor and propose the FF-HSG and FF-HMISG algorithms.…”

Section: The Hmisg Algorithmmentioning

confidence: 99%

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

Xiao

2019

121

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.

“…The proposed algorithms in this paper can combine some mathematical tools [38][39][40][41] and optimization strategies [42][43][44][45][46] and statistical techniques [47][48][49][50] to explore new identification methods of other linear and nonlinear stochastic systems and can be applied to other fields 51-54 such as information processing and communication. [55][56][57][58] The introduction of the innovation length increases the number of data used in the algorithm compared with the O-FG algorithm. Although the parameter estimation accuracy can be effectively improved, too long innovation length leads to a large amount of computation.…”

Section: The O-mifg Algorithmmentioning

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.