Supplementary damping controller design for FACTS devices using the Fuzzy Elman Wavelet Neural Network

Abstract

AbstractThe occurrence of inter‐area oscillations in a power system is challenging to prevent. Thus, it is imperative to dampen inter‐area oscillations due to their potential to cause various instability problems. In this study, the Fuzzy Elman Wavelet Neural Network (FEWNN) was created as a novel neuro‐fuzzy network that combines the beneficial features of the Wavelet Neural Network (WNN), Takagi‐Sugeno‐Kang (TSK) fuzzy model, and cross‐coupled feedbacks inspired by the Elman Neural Network (ENN). Compared to alternative fuzzy wavelet networks, the representations of high‐order nonlinear dynamic systems in the proposed novel FEWNN are more effective, the training speed and computational power are greater, and interval type‐2 fuzzy membership functions are able to manage uncertainties in the power system. This paper uses FEWNN to develop a novel method for damping inter‐area oscillations caused by flexible alternating current transmission systems (FACTS) employing a supplementary damping controller (SDC) based on direct adaptive control theory (DACT). The proposed SDC is integrated into a Static Synchronous Series Compensator (SSSC) in a two‐machine two‐area power system and Kundor's four‐machine two‐area power system, as well as a Unified Power Flow Controller (UPFC) in Kundor's four‐machine two‐area power system. In conclusion, the damping power of the system is compared to that of other SDCs presented in recent studies. The MATLAB/SIMULINK environment was used to conduct all simulations for this work.