Fuzzy inference system-based neural network controller of the UPFC

This paper focuses on the control of the power flow through a transmission line using a PWM-based UPFC. The dynamic model of the UPFC has been developed using Park??s transformation. The selected reference frame reduces the control of the real and reactive power flows to the control of the d- and q-axis, respectively. In the present paper a neuro-fuzzy controller is proposed to regulate line currents in both the shunt and series inverters of the UPFC. The controller ensures robust performance and internal stability of the UPFC against power system disturbances and unstructured parameter variations. Neural networks are good at pattern recognition, but not good at explaining how they reach their decisions. Fuzzy logic systems, which can reason with imprecise information, are good at explaining their decisions but they cannot automatically acquire the rules they use to make those decisions. These limitations have been a central driving force behind the creation of intelligent hybrid systems where two or more techniques combined in a manner that overcomes the limitations of individual techniques. This is the approach taken in this paper.