Development of adaptive control methodologies and algorithms for nonlinear dynamic systems based on u-control framework

Inspired by the U-model based control system design (or called U-control system design), this study is mainly divided into three parts. The first one is a U-model based control system for unstable non-minimum phase system. Pulling theorems are proposed to apply zeros pulling filters and poles pulling filters to pass the unstable non-minimum phase characteristics of the plant model/system. The zeros pulling filters and poles pulling filters derive from a customised desired minimum phase plant model. The remaining controller design can be any classic control systems or U-model based control system. The difference between classic control systems and U-model based control system for unstable non-minimum phase will be shown in the case studies.Secondly, the U-model framework is proposed to integrate the direct model reference adaptive control with MIT normalised rules for nonlinear dynamic systems. The U-model based direct model reference adaptive control is defined as an enhanced direct model reference adaptive control expanding the application range from linear system to nonlinear system. The estimated parameter of the nonlinear dynamic system will be placement as the estimated gain of a customised linear virtual plant model with MIT normalised rules. The customised linear virtual plant model is the same form as the reference model. Moreover, the U-model framework is design for the nonlinear dynamic system within the root inversion.Thirdly, similar to the structure of the U-model based direct model reference adaptive control with MIT normalised rules, the U-model based direct model reference adaptive control with Lyapunov algorithms proposes a linear virtual plant model as well, estimated and adapted the particular parameters as the estimated gain which of the nonlinear plant model by Lyapunov algorithms. The root inversion such as Newton-Ralphson algorithm provides the simply and concise method to obtain the inversion of the nonlinear system without the estimated gain. The proposed U-model based direct control system design approach is applied to develop the controller for a nonlinear system to implement the linear adaptive control. The computational experiments are presented to validate the effectiveness and efficiency of the proposed U-model based direct model reference adaptive control approach and stabilise with satisfied performance as applying for the linear plant model

Adaptive load frequency control of electrical power systems

The thesis describes Load Frequency Control techniques which may be used for real-time on-line control of large electrical power systems. Traditionally the frequency control of power systems has been carried out using standard fixed parameter control schemes, which give control over the immediate steady- state error and the long term accumulated frequency error, but do not account for the fact that system conditions can alter due to the change in consumer load and generating patterns. The thesis presents a method of controlling the system frequency using adaptive control techniques, which ensure that optimal control action is calculated based on the present system conditions. It enables the system operating point to be monitored so that optimal control may continue to be calculated as the system operating point alters. The proposed method of frequency control can be extended to meet the problems of system interconnection and the control of inter-area power flows. The thesis describes the work carried out at Durham on a fixed parameter control scheme which led to the development of an adaptive control scheme. The controller was validated against a real-time power system simulator with full Energy Management software. Results are also presented from work carried out at the Central Electricity Research Laboratories under the C.A.S.E award scheme. This led to the development of a power system simulator, which along with the controller was validated on-line with the Dispatch Project used by the Central Electricity Generating Board

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

Novel Algorithms for Modern Power Systems

The restructuring of the electric power industry has brought about many interesting and new problems to be solved by researchers. Some of the problems have been considered and suitable intelligent techniques that have been developed are described in this thesis. A new technique using wavelet transform and neural networks for fault location and protection of a practical tee-circuit has been developed. Fault simulation is carried out using EMTP software. The waveforms obtained from the simulation are then used in wavelet analysis to generate patterns for training and validation, which is carried out using Radial Basis Function network. Dynamic Protection Modelling (DPM) software developed by the University of Strathclyde, Centre for Electrical Power Engineering is used for the evaluation of relay settings. A new approach is proposed to use object oriented techniques and improved genetic algorithms in developing software to estimate generator excitation control system parameters. Simulation studies are carried using data representing a generator, with its transformer connected to an infinite busbar. Application of evolutionary programming to Optimal Power Flow is another technique that is proposed in this work. The objective is to minimize fuel cost keeping the system secure under both normal and contingent states. Studies are carried out using the IEEE - 30 bus test system. A short-term load-forecasting model using Artificial Neural Networks and Genetic Algorithm has been developed and tested on data obtained from a power company. It is found that the time taken to obtain a satisfactory solution is long, as the problem is very complex. This points in the direction of evolutionary computing being integrated with parallel processing techniques to solve such practical problems. A novel approach to the formulation and evaluation of transmission loss and line flow through a set of new loss coefficients and distribution factors respectively which are efficient, exact and robust and suitable for real time application is proposed. These loss coefficients and distribution factors are generated from the available load flow solution with trivial computational burden. Results on IEEE Test systems show that the coefficients need not be re-evaluated for wide changes in system operating condition

Technology 2000, volume 1

The purpose of the conference was to increase awareness of existing NASA developed technologies that are available for immediate use in the development of new products and processes, and to lay the groundwork for the effective utilization of emerging technologies. There were sessions on the following: Computer technology and software engineering; Human factors engineering and life sciences; Information and data management; Material sciences; Manufacturing and fabrication technology; Power, energy, and control systems; Robotics; Sensors and measurement technology; Artificial intelligence; Environmental technology; Optics and communications; and Superconductivity

Argonne National Laboratory Reports

This sixth symposium covers process control processes and issues involved in the conversion of fossil fuels into synthetic fuels