Load frequency control of two area power systems using optimised ANFIS controller

In this paper, novel, innovative Adaptive Neuro-Fuzzy Inference Systems (ANFIS) have been designed for the application of Load Frequency Control (LFC) in a nonlinear Two Area Power System (TAPS) consisting of a non-reheat thermal plant as a single source. Firstly, the training and testing data of the ANFIS controller is obtained by tuning the TAPS using Honey Badger Algorithm (HBA) for each area. Then this data is used to train two ANFIS controllers by taking Area Control Error (ACE) and derivative of ACE as inputs. Finally, the time response analysis is performed for the system's frequency deviation and tie-line power change. The critical parameters such as Settling Time, Peak Undershoot, Peak Overshoot, and Integral of Time-weighted Absolute Error (ITAE) are studied under 5% of load disturbance of the two-area power system. The obtained results show the better performance of the proposed ANFIS controllers compared to HBA and other existing controllers available in the literature for the FLC of the two-area power system.