NEURO-FUZZY INTELLIGENT CONTROLLER FOR LFC OF A FOUR-AREA POWER SYSTEM

In modern complex power systems, the problem of automatic generation control arises due to a sudden increase or decrease in load. This problem leads to instability in the system if the frequency control is not automatic, which may finally lead to system collapse. Hence, automatic control of frequency and tie-line power is significant. This research paper develops and compares the performance of an adaptive neuro-fuzzy inference system (ANFIS) controller with the conventional PID controller and the Takagi-Sugeno-Kang fuzzy logic controller for load frequency control (LFC) of a four-area power system with generation rate constraint (GRC) on turbines. The performance is compared in terms of errors, settling time and maximum undershoot of the frequency deviation for different step load changes using Matlab. The proposed ANFIS controller performs with less peak undershoot of-0.7374 Hz and a settling time of 27.9823 sec at a 4% change in load. It reduces the steady-state error to zero. Thus, the proposed controller is the most suitable LFC in energy centers. The system parameters are taken from the IEEE press, and EPRI published books.