Description
ABSTRACT
This work analyzes the relationship between artificial intelligence (AI) and photovoltaic (PV) systems. Solar energy is one of the most important renewable energies, and the investment of businesses and governments is increasing every year. AI is used to solve the most important problems found in PV systems, such as the tracking of the Max Power Point of the PV modules, the forecasting of the energy produced by the PV system, the estimation of the parameters of the equivalent model of PV modules or the detection of faults found in PV modules or cells. AI techniques perform better than classical approaches, even though they have some limitations such as the amount of data and the high computation times needed for performing the training. Research is still being conducted in order to solve these problems and find techniques with better performance. This article analyzes the most relevant scientific works that use artificial intelligence to deal with the key PV problems by searching terms related with artificial intelligence and photovoltaic systems in the most important academic research databases. The number of publications shows that this field is of great interest to researchers. The findings also show that these kinds of algorithms really have helped to solve these issues or to improve the previous solutions in terms of efficiency or accuracy.
Chapter one Table of contents
Cover page
Title page
Approval page
Dedication
Acknowledgement
Abstract
Chapter one
Introduction
1.1 background of the project
- Statement of the problem
- Aim and objectives of the project
- Scope of the study
- Significance of the study
Chapter two
Literature review
- The AI revolution in solar energy
- The role of AI algorithms in pv systems
- Benefits and challenges of AI integration in solar panel optimization
- artificial intelligence in pv systems
- Sizing of pv systems
Chapter three
Methodology
3.1 Controlling the pv panels
3.2 Maximum power point tracking
3.3 Inverter control
3.4 Sun tracking
3.5 Ir-radiance and output power forecasting
3.6 Solar Ir-radiance forecasting
3.7 Output power forecasting
3.8 Weather forecasting
3.9 fault diagnosis of photovoltaic systems
Chapter four
- Introduction
- Simulink model for mppt using fuzzy controller
- Photovoltaic model
Chapter five
- Conclusions and recommendation
References
1.0 Introduction
1.1 Background of the study
PV systems became an important topic for research. Many scientists and engineers are searching for techniques and algorithms to increase the efficiency of photovoltaic systems and hence decrease its cost. AI algorithms and techniques play a critical role in increasing PV systems efficiency. This thesis first gives a literature study about the role of AI algorithms in PV systems design, control, and fault diagnose. Then the thesis concentrates on the role of intelligent control in PV systems introducing a Simulink model implementation for a fuzzy controller of MPPT. The thesis introduces a novel reconfigurable generic fuzzy MPPT controller on FPGA. The fuzzy controller is written using VHDL code and simulated using Xillinx ISE tool. The work investigates also applying the reinforcement learning AI algorithm for the MPPT problem. A complete Simulink model for the reinforcement learning MPPT algorithm is implemented. Finally, the thesis investigates the role of AI algorithm grid connected inverter. A 9.1 kW complete PV system is implemented in Simulink. The system is tested for two scenarios 2kw loads and 12kw loads. A hardware implementation of a complete MPPT and inverter control is provided and experimental results are introduced.
The thesis is divided into six chapters; the first chapter gives first a brief introduction to the thesis then it describes in detail the thesis contribution and finally introduces the thesis outline. The second chapter presents a study on the role of AI techniques in the design, control and fault diagnose of PV systems. The chapter then gives a conclusion section and concludes the best AI algorithm for each application. The third chapter provides in detail a mathematical model for PV panels and then gives our proposed fuzzy controller of maximum power point tracking for A 60 watt PV system. The chapter provides detailed Simulink model representation and explained results. The fourth chapter presents our novel reconfigurable generic fuzzy controller implemented in FPGA. The chapter provides detailed description of the design including VHDL code and Xilinx tools simulations. The system is tested for two scenarios. Then the chapter introduces the hardware implementation of a complete grid connected PV system. chapter five covers the conclusion and recommendation.
1.2 Statement of the Problem
The world is facing an energy resources crisis. This crisis led many scientists to search for renewable energy sources. Photovoltaic systems are one of the most important renewable energy sources. Photovoltaic systems face the problem of high cost and low efficiency. This issue is the main research point for all research done in PV systems. To solve this problem, AI algorithms is used for increasing the efficiency of PV systems.
1.3 Aim and objectives of the studyThe integration of Artificial Intelligence (AI) in solar energy systems marks a revolutionary step in renewable energy management. This study covers how AI is optimizing solar energy efficiency and transforming the solar industry. The thesis also concentrates on intelligent control algorithms and their role in enhancing the efficiency of PV systems
Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. The aim of this work is to carry out a study on utilization of artificial intelligence to improve photovoltaic system efficiency. The objectives of this study are:
- To introduce an innovative control approach based on an Artificial Intelligence on solar system
- To apply AI algorithms for increasing the efficiency of PV systems.
- To study the applications of Artificial Intelligence on solar system
1.4 Scope of the Study
The integration of Artificial Intelligence (AI) in solar energy systems marks a revolutionary step in renewable energy management. This study covers how AI is optimizing solar energy efficiency and transforming the solar industry. The thesis also concentrates on intelligent control algorithms and their role in enhancing the efficiency of PV systems
1.5 Significance of the study
- This study will serve as a means of showing how AI algorithms can be used to predict maintenance needs, reducing downtime and extending the lifespan of solar panels.
- This study will serve as a means studying how AI tools can be used to analyze data to optimize the performance of solar panels and others PV components, adjusting to weather conditions and other variables.
- Finally, this study will also cover how AI can be used accurately in forecasting energy production, aiding in grid management and energy distribution.
CHAPTER FIVE
Conclusion and recommendation
5.1 Conclusion
The first and most important conclusion of the thesis is that Artificial intelligence plays a critical role in the design and control of PV systems.
The thesis presented a comparison between different AI techniques in each application in terms of accuracy. This can help researchers choose suitable AI algorithm for each application.
The thesis then concentrates on the applications of intelligent control in PV systems.
We first introduced the fuzzy controller for maximum power point tracking of PV systems. The result shows the superior performances of fuzzy controller over perturb and observe algorithm.
The thesis then introduces a novel reconfigurable generic FPGA implementation of the fuzzy controller. The results show the flexibility of the proposed design with minimum overhead in area.
Then a novel reinforcement learning algorithm is introduced for the maximum power point tracking. The results show that the algorithm gains it knowledge online without prior knowledge
Last the thesis introduces fuzzy controllers as a solution for controlling grid connected inverters in PV systems. A complete 9.1 kW complete Simulink system is presented. The system is tested under two scenarios. The THD of the introduced system is .9 % in current.
Finally we built a complete grid connected PV system in hardware. The results show the application of the fuzzy controller to grid connected PV systems. A comparison between the grid voltage and the inverter voltage shows complete synchronization in frequency and amplitude between the inverter voltage and grid voltage with low THD.
5.2 Recommendation
The integration of AI in solar energy is a game-changer, offering unprecedented opportunities for efficiency, sustainability, and innovation. As we continue to explore the potential of AI, it becomes increasingly clear that it will play a pivotal role in shaping the future of solar energy.
Embracing AI in solar energy is not just about technological advancement; it’s about a commitment to a smarter, more sustainable future. As AI continues to evolve, it promises to unlock new possibilities in solar energy optimization, making renewable energy more accessible and efficient for everyone.
