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2011, vol. 66, iss. 3, pp. 435-442
article language: Serbian
document type: Original Scientific Paper
published on: 27/07/2011
Robust parameter optimization of power system stabilizers by neural network
aTehnička škola, Čačak
bUniversity of Kragujevac, Technical Faculty, Čačak


In this paper, the method for optimal tuning of the power system stabilizers (PSSs) is proposed. These devices usually are the main resource for small signal stability of low frequency oscillations in power systems. The method is based on the linearized power flow model and uses system's eigenvalues (determined by dumping coefficient and frequency of oscillations), damping ratio and participation factor for determining the influence of state variables to the eigenvalue. Since the optimization model is mathematically very complex (due difficulties in defining the (non)linear relation between optimization criterion and PSS's parameters), application of Artificial Neural Networks (ANNs) is proposed. In that way, the optimization is standardized to two typical ANN-based steps: training and application. Also, the proposed robust optimization based on the parallel structure of ANNs enables to cover the different operational conditions in power system. The results of the proposed methodology are shown for standard 39-node New England test example.


power system stabilizers; stability; small signal disturbances; parameter optimization; robust optimization; artificial neural networks


*** Ulazni podaci za New England test system.
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