Comparative Performance Evaluation of Feedback Error Learning and Adaptive Neuron-PI Controllers for Small-Signal Stability Enhancement in a Single-Machine Infinite-Bus Power System

Abstract

Small-signal stability remains a fundamental challenge in the operation of modern power systems, particularly as grid complexity increases with the integration of highly dynamic power sources. Conventional Power System Stabilizers (CPSSs) have been widely adopted to improve damping performance; however, their effectiveness deteriorates when the operating conditions deviate from the design point. Consequently, intelligent adaptive control techniques have attracted significant attention owing to their ability to adjust controller parameters online in response to system dynamics. This paper presents a comprehensive comparative investigation of two intelligent adaptive control strategies, namely the Feedback Error Learning (FEL) controller and the Adaptive Neuron-Proportional-Integral (Neuron-PI) controller, for enhancing the small-signal stability of a Single-Machine Infinite-Bus (SMIB) power system. Unlike previous studies that investigated each controller independently, the proposed work evaluates both controllers under identical operating conditions, using the same SMIB model, disturbances, and simulation parameters to ensure a fair and objective comparison. The performance of both controllers is evaluated using MATLAB-SIMULINK 2025 software for multiple operating conditions. The simulation results demonstrate that the FEL controller exhibits superior adaptive learning characteristics and excellent robustness under varying operating conditions.