Data filtering-based least squares iterative algorithm for Hammerstein nonlinear systems by using the model decomposition

This paper focuses on the iterative identification problems for a class of Hammerstein nonlinear systems. By decomposing the system into two fictitious subsystems, a decomposition-based least squares iterative algorithm is presented for estimating the parameter vector in each subsystem. Moreover, a data filtering-based decomposition least squares iterative algorithm is proposed. The simulation results indicate that the data filtering-based least squares iterative algorithm can generate more accurate parameter estimates than the least squares iterative algorithm

Partially coupled gradient estimation algorithm for multivariable equation-error autoregressive moving average systems using the data filtering technique

System identification provides many convenient and useful methods for engineering modelling. This study targets the parameter identification problems for multivariable equation-error autoregressive moving average systems. To reduce the influence of the coloured noises on the parameter estimation, the data filtering technique is adopted to filter the input and output data, and to transform the original system into a filtered system with white noises. Then the filtered system is decomposed into several subsystems and a filtering-based partially-coupled generalised extended stochastic gradient algorithm is developed via the coupling concept. In contrast to the multivariable generalised extended stochastic gradient algorithm, the proposed algorithm can give more accurate parameter estimates. Finally, the effectiveness of the proposed algorithm is well demonstrated by simulation examples

Gradient-based iterative parameter estimation for bilinear-in-parameter systems using the model decomposition technique

The parameter estimation issues of a block-oriented non-linear system that is bilinear in the parameters are studied, i.e. the bilinear-in-parameter system. Using the model decomposition technique, the bilinear-in-parameter model is decomposed into two fictitious submodels: one containing the unknown parameters in the non-linear block and the other containing the unknown parameters in the linear dynamic one and the noise model. Then a gradient-based iterative algorithm is proposed to estimate all the unknown parameters by formulating and minimising two criterion functions. The stochastic gradient algorithms are provided for comparison. The simulation results indicate that the proposed iterative algorithm can give higher parameter estimation accuracy than the stochastic gradient algorithms

Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems

This paper investigates parameter estimation problems for multivariable controlled autoregressive autoregressive moving average (M-CARARMA) systems. In order to improve the performance of the standard multivariable generalized extended stochastic gradient (M-GESG) algorithm, we derive a partially coupled generalized extended stochastic gradient algorithm by using the auxiliary model. In particular, we divide the identification model into several subsystems based on the hierarchical identification principle and estimate the parameters using the coupled relationship between these subsystems. The simulation results show that the new algorithm can give more accurate parameter estimates of the M-CARARMA system than the M-GESG algorithm

Highly computationally efficient state filter based on the delta operator

The Kalman filter is not suitable for the state estimation of linear systems with multistate delays, and the extended state vector Kalman filtering algorithm results in heavy computational burden because of the large dimension of the state estimation covariance matrix. Thus, in this paper, we develop a novel state estimation algorithm for enhancing the computational efficiency based on the delta operator. The computation analysis and the simulation example show the performance of the proposed algorithm

Analysis of modern approaches for the prediction of electric energy consumption

A review of modern methods of forming a mathematical model of power systems and the development of an intelligent information system for monitoring electricity consumption. The main disadvantages and advantages of the existing modeling approaches, as well as their applicability to the energy systems of Ukraine and Kazakhstan, are identified. The main factors that affect the dynamics of energy consumption are identified. A list of the main tasks that need to be implemented in order to develop algorithms for predicting electricity demand for various objects, industries and levels has been developed

Improved EMD-Based Complex Prediction Model for Wind Power Forecasting

As a response to rapidly increasing penetration of wind power generation in modern electric power grids, accurate prediction models are crucial to deal with the associated uncertainties. Due to the highly volatile and chaotic nature of wind power, employing complex intelligent prediction tools is necessary. Accordingly, this article proposes a novel improved version of empirical mode decomposition (IEMD) to decompose wind measurements. The decomposed signal is provided as input to a hybrid forecasting model built on a bagging neural network (BaNN) combined with K-means clustering. Moreover, a new intelligent optimization method named ChB-SSO is applied to automatically tune the BaNN parameters. The performance of the proposed forecasting framework is tested using different seasonal subsets of real-world wind farm case studies (Alberta and Sotavento) through a comprehensive comparative analysis against other well-known prediction strategies. Furthermore, to analyze the effectiveness of the proposed framework, different forecast horizons have been considered in different test cases. Several error assessment criteria were used and the obtained results demonstrate the superiority of the proposed method for wind forecasting compared to other methods for all test cases.© 2020 Institute of Electrical and Electronics Engineersfi=vertaisarvioitu|en=peerReviewed

State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors

This paper considers the state estimation problem of bilinear systems in the presence of disturbances. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems. It is well known that the extended Kalman filter (EKF) is proposed based on the Taylor expansion to linearize the nonlinear model. In this paper, we show that the EKF method is not suitable for bilinear systems because the linearization method for bilinear systems cannot describe the behavior of the considered system. Therefore, this paper proposes a state filtering method for the single-input–single-output bilinear systems by minimizing the covariance matrix of the state estimation errors. Moreover, the state estimation algorithm is extended to multiple-input–multiple-output bilinear systems. The performance analysis indicates that the state estimates can track the true states. Finally, the numerical examples illustrate the specific performance of the proposed method

State filtering and parameter estimation for two input two output systems with time delay

This paper focuses on presenting a new identification algorithm to estimate the parameters and state variables for two-input two-output dynamic systems with time delay based on canonical state space models. First, the related input-output equation is determined and transformed into an identification oriented model, which does not involve in the unmeasurable states, and then a residual based least squares identification algorithm is presented for the estimations. After the parameters being estimated, the system states are subsequently estimated by using the estimated parameters. Through theoretical analysis, the convergence of the algorithm is derived to provide assurance for applicability. Finally, a selected simulation example is given for a meaningful case study to show the effectiveness of the proposed algorithm