Investigating Emerging New Photovoltaic Technologies

Abstract

The rising energy demand due to industrialization and urbanization is primarily met by depleting conventional sources like oil, gas, and coal, leading to environmental issues such as air pollution, global warming, and acid rain. Renewable energy, particularly solar energy via photovoltaic (PV) systems, offers a sustainable alternative. PV systems convert solar energy into electricity and operate most efficiently at their Maximum Power Point (MPP), influenced by factors like irradiance, temperature, and load. Effective energy extraction requires advanced converter designs with high efficiency and low voltage ripple. However, PV panels degrade over time due to outdoor exposure, and current Maximum Power Point Tracking (MPPT) techniques, including Fuzzy Logic Control (FLC), Particle Swarm Optimization (PSO), Perturb & Observe (P&O), and Artificial Neural Network (ANN), face limitations such as Total Harmonic Distortion (THD). To address these challenges, innovative control methods are necessary. Researching PV energy potential in diverse regions like Central Europe and Indochina is essential, as both regions are committed to renewable energy transitions. Despite moderate solar irradiance, these regions offer opportunities for tailored PV system designs, economic development, and energy diversification. By optimizing PV energy potential, they can contribute to global sustainability goals. The thesis addresses these challenges, structured as follows: Chapter 2 and 3 review related works, Chapter 4 explores PV energy potential in Central Europe and Indochina, Chapter 5 presents a proposed control system and experimental results, and Chapter 6 concludes the study. Research Objectives Objective 1: Existing MPPT techniques struggle under dynamic weather conditions, leading to inefficient energy harvesting. This thesis proposes a novel approach to overcome these issues by: • Developing a Current Sensorless System with Modtanh Activated Physical Neural Network (MAPNN). • Enhancing daily efficiency using the Beta Distributed Point Estimation Technique (BDPET). • Improving converter control with the Chinese Remainder Theorem - Puzzle Optimization Algorithm - Tuned PID Controller (CRT-POA-PID). Objective 2: Optimizing MPPT methods alone is insufficient without understanding regional PV energy potential. This thesis investigates solar energy potential in Central Europe and Indochina, emphasizing geographic diversity, energy transition goals, and economic opportunities. By studying these regions, the research aims to support renewable energy development, technological advancements, and sustainable economic growth. In summary, the thesis develops advanced MPPT techniques and explores PV energy potential in key regions, contributing to global renewable energy efforts and sustainable development.