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.
W. Liu et al., "Online Identification of Turbogenerator's Dynamics using a Neuro-Identifier," Proceedings of the Intelligent Systems Applications to Power Systems, 2003. ISAP 2003, Institute of Electrical and Electronics Engineers (IEEE), Aug 2003.
Intelligent Systems Applications to Power Systems, 2003. ISAP 2003
Electrical and Computer Engineering
Keywords and Phrases
Neural Networks; Online Identification; Power System; Turbogenerators
Article - Journal
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