Robust Fuzzy Control for Uncertain Nonlinear Power Systems

Tawfik Guesmi, Badr M. Alshammari, Yosra Welhazi, Hsan Hadj Abdallah and Ahmed Toumi
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Tawfik Guesmi: Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia
Badr M. Alshammari: Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia
Yosra Welhazi: Department of Electrical Engineering, National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia
Hsan Hadj Abdallah: Department of Electrical Engineering, National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia
Ahmed Toumi: Department of Electrical Engineering, National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia

Mathematics, 2022, vol. 10, issue 9, 1-26

Abstract: 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).

Keywords: fuzzy control; uncertainty nonlinear systems; Takagi-Sugeno fuzzy models; linear matrix inequalities (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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