Robust Fuzzy Control for Uncertain Nonlinear Power Systems

This paper presents a new control technique based on uncertain fuzzy models for handling uncertainties in nonlinear dynamic systems. This approach is applied for the stabilization of a multimachine power system subject to disturbances. In this case, a state-feedback controller based on parallel dist...

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Bibliographic Details
Published in:Mathematics (Basel) 2022-05, Vol.10 (9), p.1463
Main Authors: Guesmi, Tawfik, Alshammari, Badr M., Welhazi, Yosra, Hadj Abdallah, Hsan, Toumi, Ahmed
Format: Article
Language:English
Subjects:
Adaptation
Control systems
Control theory
Design
Disturbances
Dynamical systems
Feedback
Feedback control
Food science
Fuzzy control
Fuzzy sets
Liapunov functions
Linear matrix inequalities
Mathematical analysis
Nonlinear control
Nonlinear dynamics
Nonlinear systems
Nonlinearity
Performance evaluation
Robust control
Set theory
Stabilization
State feedback
Systems stability
Takagi-Sugeno fuzzy models
Uncertainty
uncertainty nonlinear systems
Voltage regulators
Wind power
Wind turbines
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Summary:This paper presents a new control technique based on uncertain fuzzy models for handling uncertainties in nonlinear dynamic systems. This approach is applied for the stabilization of a multimachine power system subject to disturbances. In this case, a state-feedback controller based on parallel distributed compensation (PDC) is applied for the stabilization of the fuzzy system, where the design of control laws is based on the Lyapunov function method and the stability conditions are solved using a linear matrix inequalities (LMI)-based framework. Due to the high number of system nonlinearities, two steps are followed to reduce the number of fuzzy rules. Firstly, the power network is subdivided into sub-systems using Thevenin’s theorem. Actually, each sub-system corresponds to a generator which is in series with the Thevenin equivalent as seen from this generator. This means that the number of sub-systems is equal to the number of system generators. Secondly, the significances of the nonlinearities of the sub-systems are ranked based on their limits and range of variation. Then, nonlinearities with non-significant variations are assumed to be uncertainties. The proposed strategy is tested on the Western systems coordinating council (WSCC) integrated with a wind turbine. The disturbances are assumed to be sudden variations in wind power output. The effectiveness of the suggested fuzzy controller is compared with conventional regulators, such as an automatic voltage regulator (AVR) and power system stabilizers (PSS).
ISSN:2227-7390
2227-7390
DOI:10.3390/math10091463