The demand for renewable energy sources is accelerating as nations seek sustainable solutions to environmental challenges. Among these, Photovoltaic Water Pumping Systems (PVWPS) have emerged as significant contributors to energy independence, particularly for rural areas where access to electricity is often limited. Recent research introduced innovative strategies utilizing Artificial Neural Networks (ANNs) to maximize the performance of these systems.
The core of the study involves two controllers: one for Maximum Power Point Tracking (MPPT) and the other for Direct Torque Control (DTC) of induction motors. Both controllers are enhanced using ANN technologies, resulting in remarkable improvements. The ANN-based MPPT controller efficiently handles the varying conditions of solar irradiance, ensuring optimal extraction of solar power, regardless of environmental fluctuations. Similarly, the integration of ANN within the DTC framework significantly reduces issues related to torque and flux ripples, hyper-switching, and challenges at low speeds.
The simulation results speak volumes about the advantages of these new methods. There was a notable 75.51% reduction in flux ripples and 77.5% reduction in torque ripples, demonstrating not just statistical improvements but also yielding increased water output from the systems. For rural regions where reliable irrigation is imperative, such advancements could reshape agricultural practices.
The researchers conducted extensive simulations using MATLAB/Simulink, later validating the results through real-time implementations. The versatility of the dSPACE DS1104 board facilitated seamless transitions from theoretical models to practical applications, as the research team was able to monitor performance closely and adjust settings dynamically based on real-time data.
By utilizing ANN algorithms, the study effectively optimizes pivotal controls within the PVWPS. The proposed ANN-MPPT relies on real-time data to dynamically learn and adjust the duty cycles of the DC-DC converters, which are responsible for converting solar power to usable energy for water pumping. This leads to faster responses during variable conditions, significantly improving energy efficiency.
On the other hand, the ANN-DTC innovative design replaces conventional components of the torque control system, including hysteresis comparators and the speed controller, with ANN systems. This transformation allows for easier regulation of motor torque and stator flux, ensuring consistent performance with less mechanical vibration.
Importantly, these improvements coincide with increasing interest and efforts toward sustainable water management solutions, particularly as climate change and population growth exacerbate water scarcity annually. PVWPS leveraging ANN technologies are not merely about technological advancements; they align with pressing environmental and societal needs, promising to provide reliable water sources to communities around the globe.
The study concludes with reflections on the performance of ANN enhancements over traditional controllers, underscoring their robustness amid varying operational conditions. Future research directions include overcoming computational demands and the need for high-quality datasets for effective ANN training. Such enhancements will be imperative to deploy these systems widely, especially within remote and under-resourced areas.
This research does not just present advanced controllers for PVWPS; it places them within the broader narrative of achieving energy sustainability and resilience, marking meaningful progress for rural communities facing energy poverty.