B.W. Hogg

A Local Model Networks Based Multivariable Long-Range Predictive Control Strategy for Thermal Power Plants

Load-cycling operation of thermal power plants leads to changes in operating point right across the whole operating range. This results in non-linear variations in most of the plant variables. This paper investigates methods to account for non-linearities without resorting to on-line parameter estimation as done in self-tuning control. A constrained multivariable long range predictive controller (LRPC), based on generalised predictive control (GPC) algorithm, is designed and implemented in a simulation of 200 MW oil-fired drum-boiler thermal power plant. In order to take into account system non-linearity, the LRPC is evaluated using two types of predictive models: approximate single global linear models and local model networks (LMN). As a simpler alternative, single global linear ARIX models were identified off-line with data generated by running the plant simulation over a load profile covering the entire operating range along with suitable PRBS signals superimposed on controls. For more accurate long-range prediction, networks of dynamic local linear models, identified after dividing the whole operating region into a number of zones, were created. The control strategy gives impressive results, when used in controlling main steam temperature and pressure and reheat steam temperature during large rate of load changes right across the operating range. The improvements are apparent in both constant-steam-pressure as well as variable-steam-pressure modes of plant operation. The results obtained with LMNs based LRPC compare favourably to the those obtained with global model based LRPC.

Multivariable generalized predictive control of a boiler system

An application of a multiloop generalized predictive control (GPC) scheme to achieve self-tuning control of superheat pressure and steam temperatures in a 200 MW power station drum boiler are presented. Controllers have been designed and evaluated using a detailed nonlinear boiler model which is well established and validated. Results illustrating the performance of the plant with GPC are presented and compared with conventional PI control. The results show that substantial improvements in control can be achieved with the GPC. Steam pressure and temperature variations are greatly reduced, without offsets, and with less controller activity. >

A neural net model-based multivariable long-range predictive control strategy applied in thermal power plant control

A constrained multivariable control strategy along with its application in more efficient thermal power plant control is presented in this paper. A neural network model-based nonlinear long-range predictive control algorithm is derived, which provides offset-free closed-loop behavior with a proper and consistent treatment of modeling errors and other disturbances. A multivariable controller is designed and implemented using this algorithm. The system constraints are taken into account by including them in the control algorithm using real-time optimization. By running a simulation of a 200 MW oil-fired drum-boiler thermal power plant over a load-profile along with suitable PRBS signals superimposed on controls, the operating data is generated. Neural network (NN) modeling techniques have been used for identifying global dynamic models (NNARX models) of the plant variables off-line from the data. To demonstrate the superiority of the strategy in a MIMO case, the controller has been used in the simulation to control main steam pressure and temperature, and reheat steam temperature during load-cycling and other severe plant operating conditions.

An object-oriented power plant adaptive control system design tool

An object-oriented power plant control system design tool is proposed in this paper which integrates modelling, analysis, design, simulation, and graphical user interface into one software suite. This facility can be conveniently used to study power plant dynamic behaviour and develop modern control algorithms. A design example of an adaptive governor shows the effectiveness of the design tool. >

Co-ordinated control of synchronous generator excitation and static VAR compensator

The application of multivariable control to a synchronous generator excitation and static volt-ampere reactive (VAR) compensator system is described to design a coordinated excitation and firing angle discrete controller. It is based on a reduced-order linearized model, obtained by system identification, which represents the nonlinear dynamics around a specified operating point. The model structure was investigated by simulation studies indicating that a third-order representation was suitable. Direct comparisons of identified model responses with corresponding results from the nonlinear model show that accurate modeling can be achieved by this approach. Quantitative assessments also established that the models are satisfactory. The controller was evaluated by simulation of a nonlinear generator model, involving comparisons between the coordinated controller with several different controllers, over a wide range of operating conditions and disturbances. It is shown that the coordinated control arrangement can provide better stability, voltage control, and damping than the other schemes. >

Neural network based control for synchronous generators

In this paper, a radial basis function neural network based AVR is proposed. A control strategy which generates local linear models from a global neural model on-line is used to derive controller feedback gains based on the generalised minimum variance technique. Testing is carried out on a micromachine system which enables evaluation of practical implementation of the scheme. Constraints imposed by gathering training data, computational load, and memory requirements for the training algorithm are addressed.

Neural control of turbogenerator systems

The application of neural networks to excitation control of a synchronous generator is considered here. A radial basis function (RBF) network was constructed using a hybrid training algorithm, combining linear optimization of the output layer weights with singular-value decomposition, and non-linear optimization of the centres and widths using second-order gradient descent BFGS. The Jacobian of the RBF network was calculated to provide instantaneous linear models of the plant, which were then used to form linear controllers. Generalized minimum variance, Kalman, and internal model control schemes were implemented on an industry-standard VME platform linked to a network of Inmos transputers, and the performance of the neural models and neural control schemes were investigated on a 3 kVA laboratory micromachine system. Comparison was made with a self-tuning regulator, employing a generalized minimum variance strategy. The results presented illustrate that not only is it possible to successfully implement neural controllers on a generator system, but also their performance is comparable with a benchmark self-tuning controller, while avoiding the significant supervisory code needed to ensure robust operation of the self-tuning controller.

Generalized predictive control of steam pressure in a drum boiler

An application of generalized predictive control (GPC) to superheat steam pressure of a 200 MW drum boiler is presented. The system performance is evaluated by computer simulation, using a detailed nonlinear boiler model, and compared with that obtained using conventional PI (proportional-integral) control. Results show a significant improvement in performance over a wide range of operating conditions. >

Predictive co-ordinated control for power-plant steam pressure and power output

Abstract This paper describes a new predictive control strategy for power-plant steam pressure and power output control. Two methods are introduced to improve existing boilers following PID control strategies in a reheat power plant to reduce pressure variations with load changes. Firstly, a multi-input multi-output model-based predictive control (MBPC) is used to design co-ordinated control of power, pressure and water level. Secondly, the steam pressure after the governing stage is fed back as an estimate of turbine power output to replace electric power. The proposed control is tested on a real-time hybrid laboratory simulator employing a detailed nonlinear boiler model and a micro-turbogenerator. Results show the significant improvement of overall performance, compared to existing control.

A novel performance monitoring strategy for economical thermal power plant operation

A need-based integrated performance monitoring strategy is proposed to economize the operation of a thermal power plant, exploiting the immensely powerful resources of newly retrofitted modern distributed control systems (DCS). After reviewing the performance monitoring practice of a typical 200 MW oil/gas fired thermal power plant at Ballylumford, N. Ireland, the paper analyses its shortcomings and then suggests remedial measures through the proposed strategy. A simulation of this plant has been used for investigation purposes. The twin key aspects of performance monitoring, i.e. monitoring of performance indices and controllable parameters, are addressed in more effective and novel ways. The achievable best efficiency values of plant components, needed for comparative performance evaluation, are shown to be more reliably and accurately obtainable through neural network performance models. A method based on histogram plots is shown to be highly effective in monitoring the performance of plant controllers in reducing the variability of plant variables and then modifying the set-points to improve the thermal performance of the plant.