Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

¹ Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, Tamil Nadu
² Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, Tamil Nadu
³ Department of Electrical & Electronics Engineering, Faculty of Engineering and Technology, JNTUA, AP

^* Corresponding author: nireekshan222@gmail.com
ramachandran.auee@gmail.com
gv1.venkata@gmail.com

Abstract

The main objective of Load Frequency Control (LFC) is to effectively manage the power output of an electric generator at a designated site, in order to maintain system frequency and tie-line loading within desired limits, in reaction to fluctuations. The adaptive neuro-fuzzy inference system (ANFIS) is a controller that integrates the beneficial features of neural networks and fuzzy networks. The comparative analysis of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Proportional-Integral-Derivative (PID)-based methodologies demonstrates that the suggested ANFIS controller outperforms both the PID controller and the ANN controller in mitigating power and frequency deviations across many regions of a hybrid power system. Two systems are analysed and represented using mathematical models. The initial system comprises a thermal plant alongside photovoltaic (PV) grid-connected installations equipped with maximum power point trackers (MPPT). The second system comprises hydroelectric systems. The MATLAB/Simulink software is employed to conduct a comparative analysis of the outcomes produced by the controllers.