Optimal power system stabilizer tuning in multi-machine system via an improved differential evolution
Yang, G.Y., Mishra, Y., Dong, Z.Y., & Wong, K.P. (2008) Optimal power system stabilizer tuning in multi-machine system via an improved differential evolution. In Chung, M.J. & Misra, P. (Eds.) Proceedings of the 17th IFAC World Congress, IFAC, COEX, South Korea, pp. 14939-14944.
Power system stabilizer (PSS) is one of the most important controllers in modern power systems for damping low frequency oscillations. Many efforts have been dedicated to design the tuning methodologies and allocation techniques to obtain optimal damping behaviors of the system. Traditionally, it is tuned mostly for local damping performance, however, in order to obtain a globally optimal performance, the tuning of PSS needs to be done considering more variables. Furthermore, with the enhancement of system interconnection and the increase of system complexity, new tools are required to achieve global tuning and coordination of PSS to achieve optimal solution in a global meaning. Differential evolution (DE) is a recognized as a simple and powerful global optimum technique, which can gain fast convergence speed as well as high computational efficiency. However, as many other evolutionary algorithms (EA), the premature of population restricts optimization capacity of DE. In this paper, a modified DE is proposed and applied for optimal PSS tuning of 39-Bus New-England system. New operators are introduced to reduce the probability of getting premature. To investigate the impact of system conditions on PSS tuning, multiple operating points will be studied. Simulation result is compared with standard DE and particle swarm optimization (PSO).
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|Item Type:||Conference Paper|
|Keywords:||Differential Evolution, Power System Stabilizer, Modal Analysis|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Power and Energy Systems Engineering (excl. Renewable Power) (090607)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Deposited On:||04 Mar 2013 01:37|
|Last Modified:||26 Jul 2013 00:53|
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