Abstract
This paper presents an optimal tuning of Power System Stabilizers (PSS) for a multi-machine power system using Population-Based Incremental Learning (PBIL) algorithm with adaptive learning rate. the proposed (APBIL) algorithm can adjust the learning rate automatically according to the degree of evolution of the search. the objective of the design is to achieve adequate stability over a wide range of operating conditions. the proposed controller is compared with traditional PBIL with fixed learning rate (PBIL) and tested under various operating conditions. Simulation results show that the APBIL based PSS provides a more efficient search capability and gives a better damping and adequate dynamic performance of the system than the traditional PBIL based PSS. ©2010 IEEE.
Recommended Citation
K. A. Folly and G. K. Venayagamoorthy, "Optimal Tuning of System Stabilizer Parameters using PBIL with Adaptive Learning Rate," IEEE PES General Meeting, PES 2010, article no. 5589696, Institute of Electrical and Electronics Engineers, Dec 2010.
The definitive version is available at https://doi.org/10.1109/PES.2010.5589696
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Learning rate; Population-based incremental learning; Power system stabilizer
International Standard Book Number (ISBN)
978-142448357-0
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
06 Dec 2010
