ANN and ANFIS Based Control Approaches for Enhanced Performance of Solar PV Driven Water Pumping Systems Employing Quasi Z-Source Converter

Solar Photovoltaic (SPV) harnesses abundant solar energy for water pumping, reducing dependence on conventional sources and promoting sustainability. Efficient Brushless DC (BLDC) motor control and battery management ensures energy efficiency, reliability and continuous operation in standalone solar PV-based pumping systems with a quasi Z-source converter. This work presents a comprehensive system that integrates a ripple-free quasi-Z-source (QZS) converter, dc link capacitor, three-phase voltage source inverter (VSI), maximum power point controller (MPPT) and a brushless DC (BLDC) motor pump drive. The system also incorporates an enhanced power management system with a controlled storage battery connected to a bidirectional buck-boost converter. To ensure effective power conversion, the QZS converter is used to regulate the dc voltage obtained from the SPV system. An Incremental Conductance (INC) trained Artificial Neural Network MPPT (INC-ANN-MPPT) controller is utilized in the SPV-based QZS converter to extract the maximum power from the PV system. The battery charge and discharge system is controlled by a PI controller to optimize the utilization of the stored energy in battery. For the VSI's back-end operations, three different controllers have been proposed namely Variable Speed drive Dual-Tuned PID (VSDT-PID) controller, VSDT-ANN controller and VSDT-ANFIS controller. The proposed system was analyzed for different configurations and the effectiveness of proposed control algorithms and system design has been validated. The potential of SPV-based motor drive applications for sustainable energy adoption has been highlighted.