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 (GOT) artificial neural network (ANN) controllers 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 ANN is used to identify the complex nonlinear dynamics of the power system. Results are presented to show that the two COT ANN controllers can control turbogenerators under steady state as well as transient conditions and thus allow turbogenerators to operate more closely to their steady state stability limits

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

A Nonlinear Voltage Controller with Derivative Adaptive Critics for Multimachine Power Systems

Based on derivative adaptive critics, a novel nonlinear optimal voltage/excitation control for a multimachine power system is presented. The feedback variables are completely based on local measurements. Simulations on a three-machine system demonstrate that the nonlinear controller is much more effective than the conventional PID controller equipped with a power system stabilizer for improving dynamic performance and stability under small and large disturbances

Experimental Studies with Continually Online Trained Artificial Neural Network Identifiers for Multiple Turbogenerators on the Electric Power Grid

The increasing complexity of a modern power grid highlights the need for advanced system identification techniques for effective control of power systems. This paper provides a new method for nonlinear identification of turbogenerators in a 3-machine 6-bus power system using online trained feedforward neural networks. Each turbogenerator in the power system is equipped with a neuro-identifier, which is able to identify its particular turbogenerator and the rest of the network to which it is connected from moment to moment, based on only local measurements. Each neuro-identifier can then be used in the design of a nonlinear neurocontroller for each turbogenerator in such a multi-machine power system. Experimental results for the neuro-identifiers are presented to prove the validity of the concep

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

Adaptive Critic Based Neurocontroller for Turbogenerators with Global Dual Heuristic Programming

Turbogenerators are nonlinear time varying systems. This paper presents the design of a neurocontroller for such a turbogenerator that augments/replaces the traditional automatic voltage regulator (AVR) and the turbine governor using a novel technique based on the adaptive critic designs (ACDs) with emphasis on global dual heuristic programming (GDHP). Simulation results are presented to show that the neurocontroller derived with the GDHP approach is robust and its performance is better when compared with that derived with other neural network technique, especially when system conditions and configuration changes

Comparison of Heuristic Dynamic Programming and Dual Heuristic Programming Adaptive Critics for Neurocontrol of a Turbogenerator

This paper presents the design of an optimal neurocontroller that replaces the conventional automatic voltage regulator (AVR) and the turbine governor for a turbogenerator connected to the power grid. The neurocontroller design uses a novel technique based on the adaptive critic designs (ACDs), specifically on heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Results show that both neurocontrollers are robust, but that DHP outperforms HDP or conventional controllers, especially when the system conditions and configuration change. This paper also shows how to design optimal neurocontrollers for nonlinear systems, such as turbogenerators, without having to do continually online training of the neural networks, thus avoiding risks of instability

Implementation of Adaptive Critic-Based Neurocontrollers for Turbogenerators in a Multimachine Power System

This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontroller\u27s training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes in system operating conditions and configurations. This paper also shows that it is possible to design and implement optimal neurocontrollers for multiple turbogenerators in real time, without having to do continually online training of the neural networks, thus avoiding risks of instability

Dual Heuristic Programming Excitation Neurocontrol for Generators in a Multimachine Power System

The design of optimal neurocontrollers that replace the conventional automatic voltage regulators for excitation control of turbogenerators in a multimachine power system is presented in this paper. The neurocontroller design is based on dual heuristic programming (DHP), a powerful adaptive critic technique. The feedback variables are completely based on local measurements from the generators. Simulations on a three-machine power system demonstrate that DHP based neurocontrol is much more effective than the conventional PID control for improving dynamic performance and stability of the power grid under small and large disturbances. This paper also shows how to design optimal multiple neurocontrollers for nonlinear systems, such as power systems, without having to continually online train the neural networks, thus avoiding risks of instability

Adaptive Neural Network Identifiers for Effective Control of Turbogenerators in a Multimachine Power System

This paper provides a novel method for nonlinear identification of multiple turbogenerators in a five-machine 12-bus power system using continually online trained (COT) artificial neural networks (ANNs). Each turbogenerator in the power system is equipped with all adaptive ANN identifier, which is able to identify/model its particular turbogenerator and rest of the network to which it is connected from moment to moment, based on only local measurements. Each adaptive ANN turbogenerator can be used in the design of a nonlinear controller for each turbogenerator in a multimachine power system. Simulation results for the adaptive ANN identifiers are presente