@article{Taki_Shishebori_Abazari_Markadeh_2024, title={Comparison of ANFIS Based SSSC, STATCOM and UPFC Controllers for Transient Stability Improvement}, volume={4}, url={https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/comparison-of-anfis-based-sssc-statcom-and-upfc-controllers-for-transient-stability-improvement/}, DOI={10.1234/mjee.v4i4.422}, abstractNote={This paper presents the comparative performance of neuro- Fuzzy controlled Voltage Source Converters (VSC) based Flexible AC Transmission System (FACTS) devices, such as Static Synchronous Series Compensator (SSSC), Static Synchronous Compensator (STATCOM), and Unified Power Flow Controller (UPFC) in terms of improvement in transient stability. In neuro-fuzzy control method the simplicity of fuzzy systems and the ability of training in neural networks have been combined. The training data set the parameters of membership functions in fuzzy controller. This Adaptive Network Fuzzy Inference System (ANFIS) can track the given input-output data in order to conform to the desired controller. The maximization of energy function of UPFC is used as an objective function to generate the training data. Proposed method is tested on a single machine infinitive bus system to confirm its performance through simulation. The results prove the noticeable influence of ANFIS controlled UPFC on increasing Critical Clearing Time (CCT) of system.}, number={4}, journal={Majlesi Journal of Electrical Engineering}, publisher={OICC Press}, author={Taki, Forough and Shishebori, Ali and Abazari, Saeed and Markadeh, Gholamreza Arab}, year={2024}, month={Feb.}, keywords={Quadrotor, nonlinear control, Lyapunov Stability, Genetic Algorithm, Gain Tuning. ,} }