Teaching–Learning-Based Optimization of a 2DOF-PIDN Controller for Load Frequency Regulation in Multi-Source Power Systems

Abstract

This paper presents a Teaching–Learning-Based Optimization (TLBO)-tuned two-Degree-Of-Freedom Proportional–Integral–Derivative with Derivative filter (2DOF-PIDN) controller for load frequency control of an interconnected multi-source power system comprising thermal, hydro, and gas generating units. The controller parameters are optimized using the Integral Time-multiplied Square Error (ITSE) objective function to improve frequency regulation and suppress tie-line power oscillations following load disturbances. The proposed controller is implemented in MATLAB/Simulink and compared with Ant Lion Optimizer (ALO), Grasshopper Optimization Algorithm (GOA), and Water Cycle Algorithm (WCA) under identical operating conditions. System performance is evaluated through frequency deviations, tie-line power deviation, settling time, overshoot, undershoot, and objective function value. Simulation results demonstrate that the TLBO-based 2DOF-PIDN controller provides superior damping characteristics and reduced transient oscillations compared with the benchmark methods. Furthermore, the proposed approach achieves the lowest ITSE value of 1.82×10⁻⁴, indicating its effectiveness in enhancing dynamic stability and load frequency regulation in interconnected multi-source power systems.