Abstract

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's 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.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Keywords and Phrases

ACD; AVR; DHP Neurocontrollers; Innovative Integration M67 Card; PSS; TMS320C6701 Processor; Adaptive Control; Adaptive Critic-Based Neurocontrollers; Automatic Voltage Regulator; Dual Heuristic Programming; Heuristic Programming; Multimachine Power System; Multiple Turbogenerators; Neurocontroller Design; Neurocontrollers; Optimal Control; Optimal Neurocontrollers; Power System Control; Power System Stabilizer; Real-Time Systems; Robustness; Turbine Governor; Turbogenerators

International Standard Serial Number (ISSN)

1045-9227

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Full Text Link

Share

 
COinS