Applying metaheuristic optimization methods to design novel adaptive PI-type fuzzy logic controllers for load-frequency control in a large-scale power grid

Due to the complexity and diversity of large-scale power systems in practice, designing load-frequency control (LFC) strategies against load variations faces big challenges to ensure the stability and economy of the network. The focus of this paper is to design a novel adaptive PI-type fuzzy logic (FL)-based LFC architecture for solving the LFC problem in such an interconnected electric power grid. Applying 2 biologically inspired optimization methods, namely particle swarm optimization method and a genetic algorithm, the membership functions and rule base of a basic PI-type FL model were parameterized and optimized simultaneously and successfully. An online self-tuning method was adopted to adjust the output scaling factor, which significantly affects the control performances of the FL inference system. Thereafter, the proposed LFC strategy was applied to a typical 3-control-area power system with various load change conditions and generation units. Numerical simulations revealed the dynamic responses of the system frequency and tie-line power deviations verified the feasibility of the proposed controllers. It was found that such controllers, compared with the conventional PI regulators as well as other FL-based LFC models, can obtain better control performances. Major dynamic control indices, especially the overshoots and settling times, are effectively minimized to quickly recover the steady state of the network after random load changes, thus ensuring the stability and reliability of the system.