Abstract

The increasing complexity of modern power systems highlights the need for effective system identification techniques for the successful control of power system. This paper proposes a robust continually online trained neuroidentifier to predict the outputs of turbogenerator - terminal voltage and speed deviation. The inputs to the neuro-identifier are the changes of the plant's outputs and plant's inputs. It overcomes the drawback of calculating deviation signals from reference signals for different operating points in previous work. Simulation results show that the neuro-identifier can provide accurate identification under different operating conditions. Furthermore, the neuro-identifier can learn the dynamics of the system in a short period of time, which makes it suitable for use with an online adaptive controller for the control of turbogenerators.

Meeting Name

Intelligent Systems Applications to Power Systems, 2003. ISAP 2003

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Neural Networks; Online Identification; Power System

Library of Congress Subject Headings

Turbogenerators

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.


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