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An on-line self-learning power system stabilizer using a neural network method

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3 Author(s)
Shijie Cheng ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore ; Rujing Zhou ; Lin Guan

Based on the extensive theoretical analysis of a self-learning algorithm, a novel on-line neural network self-learning algorithm is proposed. This algorithm aims to learn the inverse dynamics of a controlled system. Samples can be easily obtained by the measurements. A reference model or a given orbit is used to generate ideal system responses. A scheme for on-line real-time implementation of such a controller is given. The proposed algorithm has been used to design a self-learning power system stabilizer. Simulation results show that the proposed self-learning neural network based PSS is very effective in damping out the lower frequency oscillations

Published in:

Power Systems, IEEE Transactions on  (Volume:12 ,  Issue: 2 )

Date of Publication:

May 1997

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