Coordinated Fuzzy Control of SSSC and SMES for Frequency Control in a DFIG Integrated Power System

Document Type : Research Paper

Authors

1 Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran

2 Faculty of Electrical Engineering Shahid Beheshti University

3 Faculty of Electrical and Computer Engineering (ECE), Semnan University, Semnan, Iran.

10.22075/mseee.2025.34911.1182

Abstract

 Nowadays, one of the features of developed countries is that they benefit from a highly reliable and stable electricity grid. In recent decades, the growth of power electronics technology has assisted this development. Flexible alternating current system (FACTS) devices are power electronics devices in the AC transmission grid. This paper evaluates the static synchronous series compensator (SSSC) in series with the transmission line and superconducting magnetic energy storage (SMES) placed at the end of the line to reduce grid frequency disturbances. The challenge is addressed by finding the optimal values for SSSC and SMES in the transmission grid using fuzzy logic, which is based on human decisions. Moreover, this paper investigates the effect of the presence and absence of variable-speed doubly-fed induction generator (DFIG)-type wind turbines. By increasing the penetration of variable-speed wind turbines in the grid, the equivalent system inertial is reduced, the ability to adjust the grid frequency is weakened, and the possibility of instability and even grid collapse after the frequency drop is increased. To this end, upon detection of frequency deviation in the grid, additional power is applied to the power system as a function of the grid frequency deviation by allocating a wind turbine with a control loop. In the absence of DFIG, the effect of only the optimized SMES-SSSC is investigated. This simulation is conducted using MATLAB/Simulink, and it is indicated that the error signal in determining the SMES and SSSC parameters is significantly reduced compared to previous studies, thanks to employing the fuzzy algorithm. 

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References

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History

  • Receive Date: 13 October 2024
  • Revise Date: 08 April 2025
  • Accept Date: 24 September 2025

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