Coordinated tuning of power system stabilizers based on Fourier Transform and neural networks
2012 (English)In: Electric power systems research, ISSN 0378-7796, Vol. 88, 78-88 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2012. Vol. 88, 78-88 p.
Artificial neural network, Fast Fourier Transform, Power system stabilizers, Small-signal stability, Algebraic relations, Amplitude spectra, Approximators, Arbitrary number, Classical optimization, Coordinated tuning, Fast Fourier transform (FFT), Input functions, ON dynamics, Operating condition, Optimal tuning, Optimization criteria, Optimization models, Poorly damped, Power System Stabilizer, Small signal stability, Test systems, Trust region, Fast Fourier transforms, Frequency response, Neural networks, Standby power systems, Optimization
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-95722DOI: 10.1016/j.epsr.2012.01.017ISI: 000303956000010ScopusID: 2-s2.0-84857377252OAI: oai:DiVA.org:kth-95722DiVA: diva2:529085
QC 201205292012-05-292012-05-292012-06-07Bibliographically approved