Open AccessDecentralized nonlinear synergetic power system stabilizers design for power system stability enhancement
Author(s) -
Zhao Ping,
Yao Wei,
Wang Shaorong,
Wen Jinyu,
Cheng Shijie
Publication year - 2014
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.1788
Subject(s) - control theory (sociology) , electric power system , nonlinear system , robustness (evolution) , adaptability , engineering , stability (learning theory) , transmission system , control engineering , power (physics) , computer science , transmission (telecommunications) , control (management) , physics , quantum mechanics , artificial intelligence , machine learning , ecology , biochemistry , chemistry , biology , electrical engineering , gene
SUMMARY Power system stabilizer (PSS) is widely used to damp low frequency oscillations that inherently exist in the interconnected power system and limit its transmission capacity and stability. This paper presents a novel nonlinear PSS based on synergetic control theory that has strong robustness and adaptability to external disturbances. Firstly, according to the control target of the proposed synergetic PSS (SPSS), the deviations of generator rotor speed and active power are used to combine a manifold. The control law of the SPSS is deduced based on the nonlinear model of synchronous generator using synergetic control theory subsequently. As all of the input signals of the SPSS are local measurements and independent of the parameters of the power transmission network, the decentralized control strategy can be achieved. Two case studies are undertaken on a single‐machine infinite‐bus power system and a three‐machine six‐bus power system, respectively. Simulation results demonstrate that the proposed SPSS can provide better damping performances than those of the conventional PSS under a wide range of operating conditions and disturbances. Moreover, the proposed SPSS is robust with respect to the variations and uncertainties of the system parameters. Copyright © 2013 John Wiley & Sons, Ltd.