Abstract
This paper proposes optimal load frequency control (LFC) designed by Adaptive Neuro Fuzzy Inference System (ANFIS) trained via antlion optimizer (ALO) for multi-interconnected system comprising renewable energy sources (RESs). Two systems are modeled and investigated; the first one has two plants of grid connected photovoltaic (PV) system with maximum power point tracker (MPPT) and thermal plant while the second comprises four plants of thermal, wind turbine and grid connected PV systems. ALO is employed to get the optimal gains of Proportional–Integral (PI) controller such that the integral time absolute error (ITAE) of frequency and tie line power deviations is minimized. The input and output of the optimized PI controller are used to train the ANFIS-LFC with Gaussian surface membership functions. Different load disturbances are studied and the results are compared with other reported approaches. The obtained results confirmed the accuracy and reliability of the proposed approach in designing LFC for multi-interconnected power systems.
•ALO is proposed for training ANFIS for simulating LFC installed in multi-interconnected areas with RESs.•The proposed objective function is ITAE of frequencies and tie line power deviations.•Gaussian surface membership functions are presented for ANFIS-LFC.•Sensitivity analysis of the system parameters and load disturbances are studied.•The results confirmed the quality and reliability of the proposed approach in designing LFC.