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This article studies the parameter estimation to the system response from the discrete measurement data. By constructing the dynamical rolling cost functions and using the nonlinear optimization, t...

The parameter estimation algorithms based on the dynamical response measurement data

Maximum likelihood methods are based on the probability and statistics theory, and significant for parameter estimation and system modeling. This paper combines the maximum likelihood principle with the data filtering technique for parameter estimation of a class of bilinear systems. The input-output representation of a bilinear system is derived through eliminating the state variables in the model. Then, a filtering based maximum likelihood iterative least squares algorithm is proposed for identifying the parameters of bilinear systems with colored noises by filtering the input-output data with a filter. A least squares based iterative algorithm is given for comparison. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems. The filtering based maximum likelihood iterative least squares algorithm is more accurate under different noise variance, and has higher computational efficiency.

Maximum Likelihood Least Squares Based Iterative Estimation for a Class of Bilinear Systems Using the Data Filtering Technique

Mathematical models are basic for designing controller and system identification is the theory and methods for establishing the mathematical models of practical systems. This paper considers the parameter identification for Hammerstein controlled autoregressive systems. Using the key term separation technique to express the system output as a linear combination of the system parameters, the system is decomposed into several subsystems with fewer variables, and then a hierarchical least squares (HLS) algorithm is developed for estimating all parameters involving in the subsystems. The HLS algorithm requires less computation than the recursive least squares algorithm. The computational efficiency comparison and simulation results both confirm the effectiveness of the proposed algorithms.

A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation

The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle

This paper studies the parameter estimation problem of Hammerstein output error autoregressive (OEAR) systems. According to the maximum likelihood principle and the Levenberg–Marquardt optimization method, a maximum likelihood Levenberg–Marquardt recursive (ML-LM-R) algorithm using the varying interval input–output data is proposed. Furthermore, a stochastic gradient algorithm is also derived in order to compare it with the proposed ML-LM-R algorithm. Two numerical examples are provided to verify the effectiveness of the proposed algorithms.

Parameter estimation algorithms for Hammerstein output error systems using Levenberg–Marquardt optimization method with varying interval measurements☆

This paper addresses the problem of recursive identification of Wiener nonlinear systems whose linear subsystems are observable state-space models. The maximum likelihood principle and the recursive identification technique are employed to develop a recursive maximum likelihood identification algorithm which estimates the unknown parameters and the system states interactively. In comparison with the developed recursive maximum likelihood algorithm, a recursive generalized least squares algorithm is also proposed for identification of such Wiener systems. The performance of the developed algorithms is validated by two illustrative examples.

A Recursive Identification Algorithm for Wiener Nonlinear Systems with Linear State-Space Subsystem

The Wiener nonlinear system is composed of a linear dynamic subsystem in series with a static nonlinear subsystem. This type of system is widely found in the petroleum, chemistry, thermal and other process industries. It is of great significance to obtain the parameter estimates of the Wiener systems. This paper studies the identification problem of the Wiener time delay nonlinear system. Based on the gradient search principle, a stochastic gradient identification algorithm and a gradient-based iterative identification algorithm are derived. Furthermore, a linearly decreasing weight particle swarm iterative identification algorithm is also proposed for the discussed Wiener time delay systems. Finally, a numerical example and two application cases are given for validating the feasibility of the three identification methods. The results demonstrate that the three algorithms can identify the unknown parameters of the Wiener model effectively. Moreover, the linearly decreasing weight particle swarm iterative identification algorithm behaves much better than the stochastic gradient and the gradient-based iterative algorithms in accuracy and convergence speed.

Parameter Estimation of Wiener Systems Based on the Particle Swarm Iteration and Gradient Search Principle

Power electronic devices interfaced doubly fed induction generators (DFIGs) cause a reduction in the power system inertia and further the power system becomes vulnerable to severe disturbances. DFIGs provide fast frequency response (FFR) strategies to the power system by increasing the output power. Nevertheless, these strategies produce a large frequency second drop or a slow rotor speed restoration. This article suggests a FFR strategy of a DFIG based on variable power point tracking control to boost the frequency support capability with grid-friendly rotor speed recovery. Furthermore, the benefit of the proposed strategy when participating in FFR is calculated. The test results illustrate that the proposed FFR strategy enhances the performance of the frequency nadir, size of SFD, and benefits of auxiliary FFR service. Hence, the proposed strategy provides a promising solution of ancillary service for FFR from a wind turbine generator to power systems.

Fast Frequency Response of a DFIG Based on Variable Power Point Tracking Control

Aiming at the problems of complex outdoor background lighting conditions, unsatisfactory moving target features, and high degree of background fusion of wind turbines with large target background, the proposed wind turbine detection and tracking method based on deep learning convolutional neural network is proposed. This paper uses migration learning technology to use a pre-trained SSD model trained with COCO data sets and makes a dedicated localized wind turbine dataset . Through data enhancement, the neural network model can effectively improve the accuracy of wind turbine identification and the average accuracy remains above 96%. The fast recognition speed enables detection and tracking of the fan target in aerial video quickly and accurately.

Liang Hua

Papers

Parameter estimation algorithms for Hammerstein output error systems using Levenberg–Marquardt optimization method with varying interval measurements☆

A Recursive Identification Algorithm for Wiener Nonlinear Systems with Linear State-Space Subsystem

Parameter Estimation of Wiener Systems Based on the Particle Swarm Iteration and Gradient Search Principle

Fast Frequency Response of a DFIG Based on Variable Power Point Tracking Control

Motion Tracking Detection and Tracking Technology Based on Aerial Video