Optimal Design of Multi-Machine Power System Damping Controller Using Neuro-Fuzzy Controller Based Stabilizer

To improve the capacity of the power system damping, a power system stabilizers (PSSs) is typically the chosen option. Traditional PSSs performance fails to provide superior damping under other operating circumstances. Many adaptive control approaches have been proposed to solve this problem, but they are both difficult and expensive. Implementing a PSSs damping controller requires extensive system modeling, placing a heavy computational burden on the system, and is a time-consuming process. A Neuro-Fuzzy Controller (NFC), a damping controller that can take the place of the FFA-PSS controller, was proposed to address these shortcomings. The dynamic model of 10-machine test systems under multiple operating conditions was developed with the presence of NFC in SIMULINK. After carrying out the time-domain simulation on the New England IEEE test system, the proposed NFC model produced a 13% rotor speed and 43% rotor angle respective angle stability enhancement in G5 based on the time to settle when compared to the FFA-PSS model. Our work has led us to conclude that the simulation results with the proposed NFC model shows that the Electromechanical Modes (EMs) eigenvalues were shifted to the left in S-plane and the system damping was greatly enhanced.