Contribution to Diagnostic and Tolerant Control of Photovoltaic Pumping System

In the face of escalating global energy demands and environmental concerns stemming from fossil fuel reliance, renewable energies offer a promising solution. Photovoltaic (PV) energy production, notably through solar PV pumping systems, has emerged as a clean and efficient alternative. These systems, using solar panels to power water pumps, provide reliable water supply solutions, particularly in remote and off-grid areas. However, technical challenges, including faults and failures, can disrupt the performance of solar PV pumping systems. To address these challenges, fault detection and tolerant control techniques are crucial. These techniques aim to classify and diagnose faults systematically, allowing for timely maintenance and repair. They also enable the system to adapt and continue operating efficiently despite the presence of faults, minimizing downtime and reducing costs. The primary objectives of this research are to advance the field of solar PV pumping systems by developing comprehensive fault detection techniques and implementing fault-tolerant control strategies. These objectives include system modeling, fault construction, fault analysis, fault detection using Discrete Wavelet Transform (DWT) and Artificial Neural Networks (ANN), and fault-tolerant control using Field-Oriented Control (FOC) with adaptive Fuzzy-PID control. By addressing these objectives, this thesis seeks to enhance the reliability, efficiency, and sustainability of solar PV pumping systems, with implications for uninterrupted water supply services. These advancements in fault diagnosis and tolerant control have the potential to significantly improve the operation and maintenance of solar PV pumping systems in both remote and conventional settings, ultimately reducing costs and ensuring reliable water supply services.