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

Parameter and State Estimator for State Space Models

This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective

Novel adaptive stability enhancement strategy for power systems based on deep reinforcement learning

As the access rate of wind energy in a power system has significantly increased, stabilizing the power system has become challenging. Among these challenges, low-frequency oscillation is one of the most harmful problems, effectively resolved by adding a damping controller according to the relevant properties of the low-frequency oscillation. However, the controller often fails to adapt to the constantly changing wind energy system owing to the lack of a targeted dynamic change strategy. Thus, to address this issue, an adaptive stabilization strategy that uses a static var compensator with an additional damping controller structure is proposed. Specifically, the entire power system is equivalently represented as a generalized regression neural network, with a deep reinforcement learning algorithm called soft actor-critic introduced to train the agent based on the generalized regression neural network model. After the training process, the agent can provide additional efficient static var compensator damping controller parameters under different operating conditions, vastly improving the system stability. Simulation results verify the improved performance using the proposed strategy compared to other optimization methods, regardless of whether the low-frequency oscillations were suppressed in the time or frequency domains

Self-tuning controllers via the state space

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Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique

Previous studies revealed that Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) outperformed Vector Autoregression (VAR) when data exhibit heteroscedasticity. However, EGARCH estimation is not efficient when the data have leverage effect. Therefore, in this study the weaknesses of VAR and EGARCH were modelled using Combine White Noise (CWN). The CWN model was developed by integrating the white noise of VAR with EGARCH using Bayesian Model Averaging (BMA) for the improvement of VAR estimation. First, the standardized residuals of EGARCH errors (heteroscedastic variance) were decomposed into equal variances and defined as white noise series. Next, this series was transformed into CWN model through BMA. The CWN was validated using comparison study based on simulation and four countries real data sets of Gross Domestic Product (GDP). The data were simulated by incorporating three sample sizes with low, moderate and high values of leverages and skewness. The CWN model was compared with three existing models (VAR, EGARCH and Moving Average (MA)). Standard error, log-likelihood, information criteria and forecast error measures were used to evaluate the performance of the models. The simulation findings showed that CWN outperformed the three models when using sample size of 200 with high leverage and moderate skewness. Similar results were obtained for the real data sets where CWN outperformed the three models with high leverage and moderate skewness using France GDP. The CWN also outperformed the three models when using the other three countries GDP data sets. The CWN was the most accurate model of about 70 percent as compared with VAR, EGARCH and MA models. These simulated and real data findings indicate that CWN are more accurate and provide better alternative to model heteroscedastic data with leverage effect

System identification for complex financial system.

The mam purpose of this thesis focuses on the investigation of major financial volatility models including the relevant mean model used in the context of volatility estimation, and the development of a systematic nonlinear identification methodology for these problems. Financial volatility is one of the key aspects in financial economics and volatility modelling involves both the mean process modelling, and the volatility process modelling. Although many volatility models have been derived to approximate the volatility process, linear mean models are almost always used and to the best of our knowledge there is no application of fitting the mean process using a nonlinear model with selected structure. Based on the fact that nonlinearity has been observed in many financial market return data sets, the Non linear AutoRegression Moving Average with eXogenous input (NARMAX) modelling methodology with the term selection algorithm Orthogonal Forward Regression (OFR) is proposed to approximate the nonlinear mean process during volatility modelling. However, the assumption of a constant variance is usually violated in financial market return data. A new Weighted OFR algorithm is therefore proposed to correct for the impact of heteroskedastic noise on the term selection of the nonlinear mean model based on the assumption that the variance process is modelled by a Generalized AutoRegressive Conditional Heteroskedastic (GARCH) model. Because the weights to use are unknown, an iterative refined procedure is developed to learn the weights and to simultaneously improve the parameter estimates of both the mean and the volatility models. New validation methods are proposed to validate the nonlinear selected mean model and the volatility model. During the validation, the assumptions associated with the mean model are tested using a correlation method and the assumptions of the volatility model are tested using a Brock-Dechert-Scheinkrnan (80S) independent and identically distributed (i.i.d.) testing method. The prediction performance of the mean and volatility models is evaluated using a hold out Cross Validation (CV)method. A departure in the prediction of the volatility for the linear mean model, when using nonlinear simulated data, is successfully identified by the new validation methods and the nonlinear selected mean model passes the test. Another application of the NARAMX model, in the very new field of modelling mortality rate, is introduced. A quadratic polynomial mortality rate model selected by the OFR algorithm is developed based on the LifeMetrics male deaths and exposures data for England & Wales from the Office of National Statistics. Comparing the long term prediction of the new model with the Cairns-Blake-Dowd (CSO) statistical mortality rate model indicates the better prediction performance of the quadratic polynomial models. A back-testing method is applied to indicate the robustness of the selected NARMAX type mortality rate models. The term selection, parameter estimation, validation methods and new identification procedures proposed in this thesis open a new gateway to apply the NARMAX modelling technique in the financial area, and for mortality rate modelling to provide a new empirical practice of the NARMAX modelling method

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Study of supercritical coal-fired power plant dynamic responses and control for grid code compliance

The thesis is concerned with the study of the dynamic responses of a supercritical coal-fired power plant via mathematical modelling and simulation. Supercritical technology leads to much more efficient energy conversion compared with subcritical power generation technology so it is considered to be a viable option from the economic and environmental aspects for replacement of aged thermal power plants in the United Kingdom. However there are concerns for the adoption of this technology as it is unclear whether the dynamic responses of supercritical power plants can meet the Great Britain Grid Code requirement in frequency responses and frequency control. To provide answers to the above concerns, the PhD research project is conducted with the following objectives: to study the dynamic responses of the power plant under different control modes in order to assess its compliance in providing the frequency control services specified by the Great Britain Grid Code; to evaluate and improve the performance of the existing control loops of the power plant simulator and in this regard a controller based on the Dynamic Matrix Control algorithm was designed to regulate the coal flow rate and another controller based on the Generalized Predictive Control algorithm was implemented to regulate the temperature of the superheated steam; to conduct an investigation regarding frequency control at the power plant level followed by an analysis of the frequency control requirements extracted from the Grid Codes of several European and non-European countries. The structure and operation of the supercritical power plant was intensively studied and presented. All the simulation tests presented in this thesis were carried out by the mean of a complex 600 megawatts power plant simulator developed in collaboration with Tsinghua University from Beijing, China. The study of the conducted simulation tests indicate that it is difficult for this type of power plant to comply with the frequency control requirements of the Great Britain Grid Code in its current control method. Therefore, it is essential to investigate more effective control strategies aiming at improving its dynamic responses. In the thesis, new Model Predictive Control power plant control strategies are developed and the performance of the control loops and consequently of the power plant are greatly improved through implementation of Model Predictive Control based controllers

Nonlinear predictive restricted structure control

This thesis defines new developments in predictive restricted structure control for industrial applications. It begins by describing the augmented system for both state-space and polynomial model descriptions. These descriptions can contain the plant model, the disturbance model, and any additional essential model subsystems. It then describes the predictive restricted structure control solution for both linear and nonlinear systems in state-space form. The solution utilizes the recent development in nonlinear predictive generalized minimum variance by adding a general operator subsystem that defines nonlinear system along with the linear or the linear parameter varying output subsystem. The next contribution is the polynomial predictive restricted structure control algorithm for polynomial linear parameter varying model that may result from nonlinear equations or experimental data-driven model identification. This algorithm utilizes the generalised predictive control method to approximate and control nonlinear systems in the linear parameter varying system inputoutput transfer operator matrices. The solution is simple in unconstrained and constrained optimization solutions and required a small computing capacity. Four examples have been chosen to test the algorithms for different nonlinear characteristics. In the first three examples, state-space versions of the algorithm for the linear, the quasi-linear parameter varying and the state-dependent were employed to control the quadruple tank process, electronic throttle body, and the continuous stirred tank reactors. In the last example, the polynomial linear parameter varying restricted structure controller is used to control automotive variable camshaft timing system.This thesis defines new developments in predictive restricted structure control for industrial applications. It begins by describing the augmented system for both state-space and polynomial model descriptions. These descriptions can contain the plant model, the disturbance model, and any additional essential model subsystems. It then describes the predictive restricted structure control solution for both linear and nonlinear systems in state-space form. The solution utilizes the recent development in nonlinear predictive generalized minimum variance by adding a general operator subsystem that defines nonlinear system along with the linear or the linear parameter varying output subsystem. The next contribution is the polynomial predictive restricted structure control algorithm for polynomial linear parameter varying model that may result from nonlinear equations or experimental data-driven model identification. This algorithm utilizes the generalised predictive control method to approximate and control nonlinear systems in the linear parameter varying system inputoutput transfer operator matrices. The solution is simple in unconstrained and constrained optimization solutions and required a small computing capacity. Four examples have been chosen to test the algorithms for different nonlinear characteristics. In the first three examples, state-space versions of the algorithm for the linear, the quasi-linear parameter varying and the state-dependent were employed to control the quadruple tank process, electronic throttle body, and the continuous stirred tank reactors. In the last example, the polynomial linear parameter varying restricted structure controller is used to control automotive variable camshaft timing system