Neural network for estimating and compensating the nonlinear characteristics of nonstationary complex systems

Issued as final reportNational Science Foundation (U.S

Two Separate Continually Online-Trained Neurocontrollers for Excitation and Turbine Control of a Turbogenerator

This paper presents the design of two separate continually online trained (COT) neurocontrollers for excitation and turbine control of a turbogenerator connected to the infinite bus through a transmission line. These neurocontrollers augment/replace the conventional automatic voltage regulator and the turbine governor of a generator. A third COT artificial neural network is used to identify the complex nonlinear dynamics of the power system. Results are presented to show that the two COT neurocontrollers can control turbogenerators under steady-state as well as transient conditions and, thus, allow turbogenerators to operate more closely to their steady-state stability limit

Adaptive Critic Designs for Optimal Control of Power Systems

The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation, turbine and flexible AC transmission systems (FACTS). The crucial factors affecting the modern power systems today is voltage and load flow control. Simulation studies in the PSCAD/EMTDC environment and realtime laboratory experimental studies carried out are described and the results show the successful control of the power system elements and the entire power system with adaptive and optimal neurocontrol schemes. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances

Swarm Intelligence for Transmission System Control

Many areas related to power system transmission require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics based swarm intelligence can be an efficient alternative. This paper highlights the application of swam intelligence techniques for solving some of the transmission system control problems

Intelligent Optimal Control of Excitation and Turbine Systems in Power Networks

The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation and turbine systems. The crucial factors affecting the modern power systems today is voltage control and system stabilization during small and large disturbances. Simulation studies and real-time laboratory experimental studies carried out are described and the results show the successful control of the power system excitation and turbine systems with adaptive and optimal neurocontrol approaches. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances

A Practical Continually Online Trained Artificial Neural Network Controller for a Turbogenerator

This paper reports on the simulation and practical studies carried out on a single turbogenerator connected to an infinite bus through a short transmission line, with a continually online trained (COT) artificial neural network (ANN) controller to identify the turbogenerator, and another COT ANN to control the turbogenerator. This identifier/controller augments/replaces the automatic voltage regulator and the turbine governor. Results are presented to show that this COT ANN identifier/controller has the potential to allow turbogenerators to operate more closely to their steady-state stability limits and nevertheless “ride through” severe transient disturbances such as three phase faults. This allows greater usage of existing power plant

Decentralized Online Neuro-Identification of Turbogenerators in a Multi-Machine Power System

This paper proposes a new and a novel technique based on Artificial Neural Networks (ANNs) for nonlinear model of turbogenerators in a multi-machine power system. Only local measurements are required by each ANN in this new method, and hence it is called decentralized neuro-identificiation. Each turbogenerator in the power system is quipped with an ANN which is able to identify (or model) its particular turbogenerator from moment to moment This information can then be used by a second ANN at each generator to enable effective control of the nonlinear non-stationary process under all operating conditions. Simulation results are presented in this paper to show the potential of this new technique for designing fkture nonlinear ANN controllers

Implementation of an Adaptive Neural Network Identifier for Effective Control of Turbogenerators

This paper describes an on-line identification technique for modelling a turbogenerator system. The dynamics of a single turbogenerator infinite bus system are modelled using an adaptive artificial neural network identifier (AANNI) based on continual online training (COT). This paper goes further to show that multilayered perceptrons with deviation signals as inputs and outputs trained using the standard backpropagation algorithm retain past learned information despite COT. Simulation and practical results are presented

A Continually Online Trained Artificial Neural Network Identifier for a Turbogenerator

The increasing complexity of modern power systems highlights the need for advanced modelling techniques for effective control of power systems. This paper presents results of simulation and practical studies carried out on identifying the dynamics of a single turbogenerator connected to an infinite bus through a short transmission line, using a continually online trained (COT) artificial neural network (ANN)