An artificial neural network based adaptive power system stabilizer

Journal Article · Mon Mar 01 00:00:00 EST 1993 · IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (United States)

DOI:https://doi.org/10.1109/60.207408· OSTI ID:6629029

Zhang, Y; Chen, G P; Malik, O P; Hope, G S ^[1]

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1. Univ. of Calgary, Alberta (Canada)

An artificial neural network (ANN) based power system stabilizer (PSS) and its application to power system are presented in this paper. The ANN based PSS combines the advantages of self-optimizing pole shifting adaptive control strategy and the quick response of ANN to introduce a new generation PSS. A popular type of ANN, the multi-layer perceptron with error back-propagation training method, is employed in this PSS. The ANN was trained by the training data group generated by the adaptive power system stabilizer (APSS). During the training, the ANN was required to memorize and simulate the control strategy of APSS until the differences are within the specified criteria. Results show that the proposed ANN based PSS can provide good damping to the power system over a wide operating range and significantly improve the dynamic performance of the system.

• OSTI ID: 6629029
• Journal Information: IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (United States), Vol. 8:1; ISSN 0885-8969
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
POWER SYSTEMS
NEURAL NETWORKS
STABILIZATION
CONTROL
DAMPING
DESIGN
EXCITATION SYSTEMS
PERFORMANCE
RESPONSE FUNCTIONS
FUNCTIONS
240100* - Power Systems- (1990-)