Tuning of power system stabilizers using an artificial neural network

Journal Article · Sun Dec 01 04:00:00 EST 1991 · IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (United States)

OSTI ID:5581483

Hsu, Y Y; Chen, C R ^[1]

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1. Dept. of Electrical Engineering, National Taiwan Univ., Taipei (TW)

This paper reports on tuning of power system stabilizers (PSS) which is investigated using an artificial neural network (ANN). To have good damping characteristics over a wide range of operating conditions, it is desirable to adapt the PSS parameters in real-time based on generator loading conditions. To do this, a pair of on-line measurements, i.e. generator real power output (P) and power factor (PF), which are representative of generator operating condition, are chosen as the input signals to the neural net. The outputs of the neural net are the desired PSS parameters. The neural net, once trained by a set of input-output patterns in the training set, can yield proper PSS parameters under any generator loading condition. Digital simulations of a synchronous machine subject to a major disturbance of three-phase fault under different operating conditions are performed to demonstrate the effectiveness of the proposed neural network.

• OSTI ID: 5581483
• Journal Information: IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (United States), Journal Name: IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (United States) Vol. 6:4; ISSN 0885-8969; ISSN ITCNE
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
240100* -- Power Systems-- (1990-)
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
DAMPING
NEURAL NETWORKS
ON-LINE SYSTEMS
POWER SYSTEMS
SIMULATION
STABILITY
STABILIZATION
TUNING