Robust LFC design using adaptive neuro-fuzzy inference-aided optimal fractional-order PIDA control for perturbed power systems with solar and wind power sources

Maintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional-order proportional-integral-derivative-acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro-fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi-area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS-FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie-line power exchange. Simulations on a New England IEEE 10-generator 39-bus power system demonstrate the approach's effectiveness under various disturbances, including random load-generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)-FOPIDA, WOA-PIDA, and WOA-ANFIS-PIDA, and recent control approaches highlight the superior performance of the WOA-ANFIS-FOPIDA method in enhancing power system stability.