Design of an Adaptive Neurofuzzy Inference Control System for the Unified Power-Flow Controller

This paper presents a new approach to control the operation of the unified power-flow controller (UPFC) based on the adaptive neurofuzzy inference controller (ANFIC) concept. The training data for the controller are extracted from an analytical model of the transmission system incorporating a UPFC. The operating points' space is dynamically partitioned into two regions: 1) an inner region where the desired operating point can be achieved without violating any of the UPFC constraints and 2) an outer region where it is necessary to operate the UPFC beyond its limits. The controller is designed to achieve the most appropriate operating point based on the real power priority. In this study, the authors investigated and analyzed the effect of the system short-circuit level on the UPFC operating feasible region which defines the limitation of its parameters. In order to illustrate the effectiveness of the control algorithm, simulation and experimental studies have been conducted using the MATLAB/SIMULINK and dSPACE DS1103 data-acquisition board. The obtained results show a clear agreement between simulation and experimental results which verify the effective performance of the ANFIC controller.

I. Nomenclature

Series-injected voltage.

Shunt-injected voltage.

Sending-end voltage.

Receiving-end voltage.

V

Voltage to the right of UPFC.

System transmission angle.

Change in real power.

Change in reactive power.

Relative in-phase series-injected voltage.

Relative perpendicular series-injected voltage.

Relative in-phase shunt voltage.

Relative perpendicular shunt voltage.

Exchanged real power.

Shunt reactive power.

KX

Short-circuit level.