B. W. Hogg

A neural network regulator for turbogenerators

A neural network (NN) based regulator for nonlinear, multivariable turbogenerator control is presented. A hierarchical architecture of an NN is proposed for regulator design, consisting of two subnetworks which are used for input-output (I-O) mapping and control, respectively, based on the back-propagation (BP) algorithm. The regulator has the flexibility for accepting more sensory information to cater to multi-input, multioutput systems. Its operation does not require a reference model or inverse system model and it can produce more acceptable control signals than are obtained by using sign of plant errors during training I-O mapping of turbogenerator systems using NNs has been investigated and the regulator has been implemented on a complex turbogenerator system model. Simulation results show satisfactory control performance and illustrate the potential of the NN regulator in comparison with an existing adaptive controller.

Identification and control of a laboratory model turbogenerator

This paper describes the application of a recursive least squares algorithm to identify a laboratory turbogenerator system. The algorithm was initially tested in a computer simulation, which indicated that identified low-order models can represent the small-signal dynamics of a non-linear system more accurately than the corresponding linearized analytical models. This was confirmed by laboratory tests, which clearly showed the difficulty of representing the system by analytical models, and the substantial improvement obtained by identification The availability of accurate low-order linear models is particularly important in the design of controllers, to facilitate the design procedures and reduce the complexity of the system. Controllers based on identified models of the plant were designed and tested over a wide range of operating conditions, and found to give very good performance.

Laboratory evaluation of adaptive controllers for synchronous generators

Abstract This paper is concerned with the development of two adaptive controllers for synchronous generators and their implementation on a laboratory micromachine system. These are a self-tuning automatic voltage regulator and a parameter adaptive excitation controller. The problems of practical operation of adaptive controllers in power systems are addressed, and results obtained from long-term laboratory tests are presented in the paper. These illustrate the effective operation of the adaptive controllers with a wide range of disturbances and varying operating conditions. It is shown that the controllers are robust, and provide consistently good performance, which is superior to that achieved with a conventional automatic voltage regulator.

On-line evaluation of auto-tuning optimal PID controller on micromachine system

Concerns the on-line evaluation of an auto-tuning optimal PID automatic voltage regulator for a synchronous generator. The design of the regulator is performed directly on a non-linear laboratory model turbogenerator, using optimization techniques, and the control strategy combines automatic voltage regulation with the functions of a power system stabilizer. The PID and stabilizing signal parameters are optimized on-line in real time, using a search procedure, with linear quadratic performance index, and only impulse responses of the system are required. Appropriate hardward and software systems have been developed, and the regulator has been extensively tested on a micromachine. Results obtained for long-term operation show that the controller can provide good performance during varying operating conditions, with a wide range of disturbances. The results have been compared with those obtained under the same operating conditions using a conventional automatic voltage regulator and stabilizer.

Optimal PID automatic voltage regulator for synchronous machines

The design and testing of an optimal PID automatic voltage regulator for synchronous machines is treated. The proposed digital PID regulator combines automatic voltage regulation with the function of a power system stabilizer. The PID and stabilizing signal parameters are optimized based on a linear quadratic performance index using the Simplex method. The design of the regulator is achieved directly without using a linearised mathematical model, and only impulse responses of the system are required during the optimization procedure. The microcomputer-based regulator with digital filters has been tested on a laboratory model turbogenerator system. Simulation and experimental results are presented, showing that the regulator provides very good performance, which is superior to that obtained with a conventional automatic voltage regulator with power system stabilizer.

On-line training of neural network model and controller for turbogenerators

The authors are concerned with the development of a neural network (NN) regulator for turbogenerator adaptive control. The NN regulator is designed based on a hierarchical architecture of neural networks. The back-propagation (BP) algorithm is used hierarchically in the NN regulator for on-line training of the turbogenerator NN model and controller. Dynamic modelling of the turbogenerator system has been investigated using the multilayer NN. The NN regulator has been implemented on a simulated complex nonlinear turbogenerator system. Simulation results evaluating the performance of the NN regulator under different operation conditions and disturbances are presented.<<ETX>>

Real-time adaptive controllers for a turbine generator

The design and practical application of series-parallel model following adaptive control techniques to a turbogenerator system is described, incorporating proportional-plus-integral and relay-plus-integral adaptation laws. An advantage of this scheme is that it does not require on-line identification, nor the solution of linear or non-linear equations in real time. The aim is to generate an adaptive command signal, such that the output of the turbogenerator will match the output of the model, even when the system is subjected to output disturbances or changes in operating conditions. The turbogenerator system consists of a synchronous generator connected to an infinite busbar through a transformer and two transmission lines in parallel. The control scheme has been thoroughly tested on a micromachine system, over a wide range of operating conditions. Typical simulation and experimental results are presented, illustrating the response to a short-circuit on the transmission network, and including comparison ...

Approaches to modelling and optimal control of a micro-alternator system

The paper describes a practical study of the application of optimal control methods to provide integrated control of the exciter and turbine of a laboratory model turbo-alternator system. Recursive least-squares identification techniques are employed to generate linear multivariate state and input/output models of the non-linear system dynamics. Comparative experimental system performance with both state and output feedback controllers, synthesized from corresponding analytical and identified models, is evaluated over a wide range of operating conditions. The results emphasize the effectiveness of integrated optimal controllers in the power system environment, and, in particular, their reduced sensitivity to changes in operating conditions and transmission line impedance compared with conventional control configurations.