Optimising PID Controllers for Multi-Area Automatic Generation Control With Improved NSGA-II

Abstract

Modern automated generation control (AGC) is increasingly complex, requiring precise frequency control for stability and operational accuracy. Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios. This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm II (SNSGA). The proposed model optimises the PID controller by minimising key performance metrics: integration time squared error (ITSE), integration time absolute error (ITAE), and rate of change of deviation (J). This approach balances convergence rate, overshoot, and oscillation dynamics effectively. A fuzzy-based method is employed to select the most suitable solution from the Pareto set. The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-II and other advanced control methods. In a two-area thermal power system without reheat, the SNSGA significantly reduces settling times for frequency deviations: 2.94s for $\Delta f_1$ and 4.98s for $\Delta f_2$, marking improvements of 31.6% and 13.4% over NSGA-II, respectively.