Solar-powered ANN-based MPPT with zeta converter for BLDC motor water pumping in rural Ethiopia for sustainable agriculture

This paper explores the design and implementation of a solar-powered water pumping system that utilizes a Brushless DC (BLDC) motor, with an Artificial Neural Network (ANN) employed for Maximum Power Point Tracking (MPPT). The extensive absence of grid connected agricultural areas in Ethiopia created a problem-water pumping with traditional motors is almost impossible, greatly reducing the productivity of agriculture and hence food security. In order to solve this problem, a photovoltaic (PV) based water pumping system has been proposed as a sustainable and efficient solution. The system comprises several key components, including a Solar Photovoltaic (SPV) array, a Zeta converter, a Voltage Source Inverter (VSI), and a BLDC motor, all working together to efficiently power a positive displacement pump. The SPV array generates electrical energy, which is then regulated by a Zeta converter to step up the voltage to 48 V, suitable for operating the BLDC motor. To ensure optimal power generation even under changing sunlight conditions, the ANN-MPPT algorithm dynamically adjusts the system for maximum efficiency. The VSI, guided by electronic commutation signals from Hall sensors, drives the BLDC motor, ensuring smooth and efficient operation. This system is designed with off-grid agricultural areas in mind, offering a dependable and eco-friendly solution for irrigation needs. Its versatility makes it suitable for a range of applications, including both domestic and agricultural uses, especially in remote locations. The proposed PV based water pumping system produced a peak power output of 410 W and maintained a stable motor speed of 2500 rpm, which is better than the traditional methods. The ANN based MPPT algorithm showed better efficiency with minimal power losses and faster system stabilization in 0.5 s. The results of this system show that it is reliable and effective in solving water pumping problems in off grid areas. The paper also includes an analysis of Ethiopia's agricultural economy, emphasizing the country's key export products and reliance on imported food. By combining the efficiency of the ANN algorithm with real-time adaptability, the system provides a reliable and sustainable water-pumping solution, making it particularly valuable for off-grid agricultural regions.