Performance Enhancement of Wind Farms Integrated with UPFC Using Adaptive Neuro-Fuzzy Inference System

The unified power flow controller (UPFC) is employed for stability improvement and voltage regulation of power systems. Adaptive Neuro-Fuzzy Inference System (ANFIS) has learning capability to solve and estimate the best solution of nonlinear functions. This paper studies the ability of ANFIS to estimate the best values of control gains of UPFC for enhancing the performance of a blended wind farm (BWF) during three phase fault. In BWF, the fixed speed squirrel cage induction generators (SCIG) and variable speed doubly fed induction generators (DFIG) are blended. The performance of BWF with ANFIS UPFC is compared with two cases, firstly BWF with UPFC controlled by artificial neural networks (ANN) (ANN UPFC), secondly BWF without UPFC. The root mean square error, RMSE, is used to measure the performance of the studied cases. The results show that the ANFIS UPFC can improve the performance of BWF. The system is achieved using the Matlab- Simulink software.

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