ANFIS-MPPT based PMSG-wind turbine interfaced with water pumping and battery management systems for optimal power flow and energy management

Abstract

This paper presents the adaptive neuro-fuzzy inference system-maximum power point tracking (ANFIS-MPPT) approach for optimizing power flow in a water system powered by a permanent magnet synchronous generator (PMSG)-wind turbine. The system uses a PMSG-based wind energy conversion system (WECS) with an ANFIS for MPPT, enabling efficient power extraction under variable wind conditions. A bidirectional SEPIC-Zeta converter interfaces a battery energy storage system (BESS) to regulate the DC-bus voltage and maintain continuous power supply to a three-phase induction motor driving the water pump. An artificial neural network (ANN)-based controller is used to manage the charging and discharging of the battery based on real-time voltage deviation. The entire system, including wind turbine, PMSG, converters, and intelligent control algorithms, is modeled and simulated in MATLAB/Simulink. Comparative analysis with conventional MPPT techniques highlights the superior performance of the proposed hybrid ANFIS-based control in terms of power flow regulation, voltage stability, and operational reliability. The results confirm that the proposed approach significantly enhances energy management and system resilience, making it suitable for standalone or remote water pumping applications powered by renewable energy sources.