New Coordinated Tuning of SVC and PSSs in Multimachine Power System Using Coyote Optimization Algorithm

Tawfik Guesmi, Badr M. Alshammari, Yasser Almalaq, Ayoob Alateeq and Khalid Alqunun
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Tawfik Guesmi: Department of Electrical Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia
Badr M. Alshammari: Department of Electrical Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia
Yasser Almalaq: Department of Electrical Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia
Ayoob Alateeq: Department of Electrical Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia
Khalid Alqunun: Department of Electrical Engineering, University of Ha’il, Ha’il 2240, Saudi Arabia

Sustainability, 2021, vol. 13, issue 6, 1-18

Abstract: This paper presents a new approach for coordinated design of power system stabilizers (PSSs) and static VAR compensator (SVC)-based controller. For this purpose, the design problem is considered as an optimization problem whose decision variables are the controllers’ parameters. Due to nonlinearities of large, interconnected power systems, methods capable of handling any nonlinearity of power networks are mostly preferable. In this regard, a nonlinear time domain based objective function is used. Then, the coyote optimization algorithm (COA) is employed for solving this optimization problem. In order to ensure the robustness and performance of the proposed controller (COA-PSS&SVC), the objective function is evaluated for various extreme loading conditions and system configurations. To show the contribution of the coordinated controllers on the improvement of the system stability, PSSs and SVC are optimally designed in individual and coordinated manners. Moreover, the effectiveness of the COA-PSS&SVC is assessed through comparison with other controllers. Nonlinear time domain simulation shows the superiority of the proposed controller and its ability in providing efficient damping of electromechanical oscillations.

Keywords: power system stabilizer; static VAR compensator; electromechanical oscillations; nonlinear time domain simulation; coyote optimization algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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