Coordinated tuning of power system stabilizers based on Fourier Transform and neural networks

This paper analyzes optimal tuning of power system stabilizers (PSSs) as the main resource for small-signal stability enhancement in power systems. The procedure is based on dynamic power system response and its frequency amplitude spectrum. Since the optimization model is very complex, there are difficulties in defining the algebraic relation between optimization criteria and PSS parameters and the authors concluded that classical optimization techniques are inappropriate for application in practice. To avoid these problems, application of artificial neural networks (ANNs) as efficient functional approximators is proposed. Optimal PSS parameters are determined by trust region based optimization, where the ANN represents an input function. Robustness of the optimization is ensured with the proposed ANN structure which considers an arbitrary number of different power system operating conditions (including single contingencies). For verification of the proposed methodology, two test systems are used: the New England-New York 68-node, 16-machine test system and the 75-machine dynamic model of the Serbian power system. Poorly damped modes of oscillation are identified and damped by installation of PSSs at appropriate locations with ANN-based optimally tuned parameters.

► We propose method for tuning of power system stabilizers (PSSs).
► Spectrum analysis is used for the system performance evaluation and optimization.
► Power system dynamic is modeled by artificial neural networks (ANNs).
► Use of ANN ensures time efficient optimization.
► Optimal PSS parameters are determined by trust region based optimization.