Neural control of turbogenerator systems

doi:10.1016/S0005-1098(97)00142-8

Automatica

Volume 33, Issue 11, November 1997, Pages 1961-1973

https://doi.org/10.1016/S0005-1098(97)00142-8 Get rights and content

Abstract

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.

References (28)

M.D. Brown et al.
Parallel adaptive control for a turbogenerator system
Control Engng Practice
(1994)
D. Flynn et al.
Expert control of a self-tuning automatic voltage regulator
Control Engng Practice
(1995)
B.A. Foss et al.
Nonlinear predictive control using local models—applied to a batch fermentation process
Control Engng Practice
(1995)
K.J. Hunt et al.
Neural networks for control systems—a survey
Automatica
(1992)
Q.H. Wu et al.
Laboratory evaluation of adaptive controllers for synchronous generators
Automatica
(1991)
P.E. An et al.
Aspects of instantaneous on-line learning rules
D.S. Broomhead et al.
Multivariable functional interpolation and adaptive networks
Complex Systems
(1988)
M.D. Brown et al.
A comparison of training methods for feed-forward neural networks
IEEE Trans. Neural Network
(1996)
C.L. Chen et al.
Hybrid learning algorithm for gaussian potential function networks
S. Chen et al.
Recursive hybrid algorithm for non-linear system identification using radial basis function networks
Int. J. Control
(1992)

D. Flynn

Expert self-tuning control for turbogenerator systems

R.S. Hingston et al.

Development of a digital automatic voltage regulator

P.K. Kalra et al.

Possible applications of neural nets to power system operation and control

Electric Power Systems Res.

(1993)

J. Kanniah et al.

Excitation control of synchronous generators using adaptive regulators

IEEE Trans. Power Apparatus Systems

(1984)

Cited by (31)

Application of artificial neural networks to micro gas turbines
2011, Energy Conversion and Management
Citation Excerpt :
In fact, to the best of our knowledge, there are no published data on the use of such methods to evaluate the emissions of an MGT. Some works report the pollutants concentration for specified working conditions, without analyzing the influence of ambient parameters or machine load on the emissions [11,12]. As regards NOx we had 9800 available patterns.
In this work, artificial neural networks (ANNs) were applied to describe the performance of a micro gas turbine (MGT). In particular, they were used (i) to complete performance diagrams for unavailable experimental data; (ii) to assess the influence of ambient parameters on performance; and (iii) to analyze and predict emissions of pollutants in the exhausts.
The experimental data used to feed the ANNs were acquired from a manufacturer’s test bed. Though large, the data set did not cover the whole working range of the turbine; ANNs and an artificial neural fuzzy interference system (ANFIS) were therefore applied to fill information gaps. The results of this investigation were also used for sensitivity analysis of the machine’s behavior in different ambient conditions.
ANNs can effectively evaluate both MGT performance and emissions in real installations in any climate, the worst R² in the validation set being 0.9962.
Estimation of furnace exit gas temperature (FEGT) using optimized radial basis and back-propagation neural networks
2008, Energy Conversion and Management
The boiler is a very important component of a thermal power plant, and its efficient operation requires continuous online information of various relevant parameters. Furnace exit gas temperature (FEGT) is one such important design/operating parameter. Knowledge of FEGT is not only useful for design of convective heating surface but also helpful for operating actions and decision making. Its online information ensures improvement in economic benefit of the power plant. Non-availability of FEGT on the operator desk greatly limits efficient operation. In this study, a novel method of estimating FEGT using neural network is presented. The training data are first generated by calculating FEGT using heat balances through various heat exchangers. Prediction accuracy and fast response are major advantages in using neural network for estimating FEGT for operator information. Two types of feed forward neural modeling networks, radial basis function and back-propagation network, were applied and compared based on their network simplicity, model building and prediction accuracy. Results are verified on practical data obtained from a 210 MW boiler of a thermal power plant.
Design and comparison of adaptive power system stabilizers based on neural fuzzy networks and genetic algorithms
2007, Neurocomputing
This paper presents two different power system stabilizers (PSSs) which are designed making use of neural fuzzy network and genetic algorithms (GAs). In both cases, GAs tune a conventional PSS on different operating conditions and then, the relationship between these points and the PSS parameters is learned by the ANFIS. ANFIS will select the PSS parameters based on machine loading conditions. The first stabilizer is adjusted minimizing an objective function based on ITAE index, while second stabilizer is adjusted minimizing an objective function based on pole-placement technique. The proposed stabilizers have been tested by performing simulations of the overall nonlinear system. Preliminary experimental results are shown.
Novel optimal neurocontrol for a synchronous generator using radial basis function neural network
2003, IFAC Proceedings Volumes (IFAC-PapersOnline)
This paper presents the design of an infinite horizon optimal neurocontroller to replace the conventional controllers such as the automatic voltage regulator and governor for the control of a synchronous generator connected to an electric power grid. The neurocontroller design uses the dual heuristic programming (DHP) algorithm, which provides the most robust control capability among the adaptive critic designs (ACDs) family. The radial basis function neural network (RBFNN) is used as the function approximator to implement the DHP. The performances of the proposed optimal neurocontroller are evaluated and its stability issue in real-time operation is analysed.
Two separate continually online-trained neurocontrollers for excitation and turbine control of a turbogenerator
2002, IEEE Transactions on Industry Applications
Comparison of heuristic dynamic programming and dual heuristic programming adaptive critics for neurocontrol of a turbogenerator
2002, IEEE Transactions on Neural Networks

View all citing articles on Scopus

View full text

Regular paperNeural control of turbogenerator systems

Abstract

Control Engng Practice

Control Engng Practice

Control Engng Practice

Automatica

Automatica

Aspects of instantaneous on-line learning rules

Multivariable functional interpolation and adaptive networks

Complex Systems

A comparison of training methods for feed-forward neural networks

IEEE Trans. Neural Network

Hybrid learning algorithm for gaussian potential function networks

Recursive hybrid algorithm for non-linear system identification using radial basis function networks

Int. J. Control

Expert self-tuning control for turbogenerator systems

Development of a digital automatic voltage regulator

Possible applications of neural nets to power system operation and control

Electric Power Systems Res.

Excitation control of synchronous generators using adaptive regulators

IEEE Trans. Power Apparatus Systems

Regular paper
Neural control of turbogenerator systems