Design and experimental evaluation tests of a Takagi–Sugeno power system stabiliser

Nogueira, Fabrício G.; Barra, Walter; Costa, Carlos Araújo da; Moraes, Anderson Roberto Barbosa de; Gomes, Marcus C. M.; Lana, Jânio José de

doi:10.1049/iet-gtd.2013.0099

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…The particular case of [2] is that it considers making a parallel fitting for the PSS and the automatic voltage regulator. In [6][7][8][9][10][11][12] models based on fuzzy logic are considered to determine an optimum fitting of the components of each controller. In [13,14] fitting strategies are proposed, respectively, for the phase compensator considering a test bench and the optimum controller of each machine according to the Heffron-Phillip model for interconnected systems.…”

Section: Introductionmentioning

confidence: 99%

Tuning of Controllers in Power Systems Using a Heuristic-Stochastic Approach

et al. 2019

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A method is proposed to fit parameters of Power System Stabilizer controllers in electromechanical multimachine power systems. The use of the Non-dominated Sorting Genetic Algorithm II heuristic method and Tabu search is considered to be initial search criteria. These methods give an approximation of the values that define the controllers. Then, the stochastic approach was used to evaluate the behavior of the parameters found when considering the system’s response to the presence of random and self-sustained in-time disturbances that affect the response of the system under steady state. The stochastic approach allows the evaluation of the system’s response through the calculation of the cost of energy loss under steady state. The method is applied to two systems: a three-machine nine-busbar system, and the Interconnected System of the Greater North (Sistema Interconectado del Norte Grande) in Chile. For these systems, the proposed methodology effectively optimized the controllers and Tabu search was shown to have a better performance than the Non-dominated Sorting Genetic Algorithm II.

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