This paper proposes an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and insolation conditions. This method uses a fuzzy logic controller applied to a DC-DC converter device. The different steps of the design of this controller are presented together with its simulation. The PV system that I chose to simulate to apply my techniques on it is stand-alone PV water pumping system. Results of this simulation are compared to those obtained by the system without MPPT. They show that the system with MPPT using fuzzy logic controller increase the efficiency of energy production from PV.
Table of Contents
I. INTRODUCTION
II. MAXIMUM POWER POINT TRACKING ALGORITHMS
III. FUZZY LOGIC MPPT CONTROLLER
A) Fuzzification
B) Inference Method
C) Defuzzification
D) Fuzzy Logic Control Simulation in MATLAB/SIMULINK
IV. SIMULATION OF PV WATER PUMP SYSTEM WITH MPPT
V. CONCLUSION
Objectives and Topics
The primary objective of this research is to enhance the energy conversion efficiency of stand-alone photovoltaic water pumping systems by implementing an intelligent Maximum Power Point Tracking (MPPT) controller based on fuzzy logic control. The study aims to address the limitations of conventional tracking methods, particularly under variable environmental conditions like temperature and insolation.
- Design and simulation of a PV array model using manufacturer data sheets.
- Implementation of an intelligent fuzzy logic controller for MPPT.
- Development of a DC-DC Cúk converter to optimize power transfer.
- Comparative analysis of system performance with and without the proposed MPPT controller.
- Evaluation of system efficiency using realistic environmental data.
Excerpt from the Book
I. INTRODUCTION
In our Arabian nation peopled areas is very small comparing to total area because fresh water resources concentrate in these areas. Now, the existing fresh water almost enough for our needs, but in the near future with increasing in people numbers it will be huge problem. On the other hand we have shining sun all the year, so we can use stand alone PV-powered water pumping system to get water in non peopled areas. Unfortunately the actual energy conversion efficiency of PV module is rather low. So to overcome this problem and to get the maximum possible efficiency, the design of all the elements of the PV system has to be optimized.
In order to increase this efficiency, MPPT controllers are used. Such controllers are becoming an essential element in PV systems. A significant number of MPPT control have been elaborated since the seventies, starting with simple techniques such as voltage and current feedback based MPPT to more improved power feedback based MPPT such as the perturbation and observation (P&O) technique or the incremental conductance technique [1-2]. Recently intelligent based controls MPPT have been introduced.
In this paper, an intelligent control technique using fuzzy logic control is associated to an MPPT controller in order to improve energy conversion efficiency.
Summary of Chapters
I. INTRODUCTION: Outlines the motivation for using PV-powered water pumping systems in arid regions and introduces fuzzy logic as a method to improve MPPT efficiency.
II. MAXIMUM POWER POINT TRACKING ALGORITHMS: Explains the necessity of MPPT by analyzing the interaction between PV module characteristics and varying load impedances.
III. FUZZY LOGIC MPPT CONTROLLER: Details the design process of the fuzzy controller, including fuzzification, inference rules, and the defuzzification process within MATLAB/SIMULINK.
IV. SIMULATION OF PV WATER PUMP SYSTEM WITH MPPT: Presents test scenarios and simulation results comparing the proposed system against direct-coupled configurations under varying atmospheric conditions.
V. CONCLUSION: Summarizes the effectiveness of the fuzzy logic-based MPPT in increasing energy production and highlights the successful simulation of the integrated PV water pumping system.
Keywords
Photovoltaic, PV, Maximum Power Point Tracking, MPPT, Fuzzy Logic Control, FLC, MATLAB, SIMULINK, Energy Efficiency, Water Pumping System, DC-DC Converter, Cúk Converter, Simulation, Renewable Energy, Intelligent Control.
Frequently Asked Questions
What is the core focus of this research?
The paper focuses on developing an intelligent control method for Maximum Power Point Tracking (MPPT) in stand-alone photovoltaic systems to improve energy conversion efficiency.
What are the primary thematic areas covered?
The key themes include PV system modeling, fuzzy logic control theory, DC-DC converter integration, and performance evaluation through simulation under variable weather conditions.
What is the main objective of the proposed controller?
The objective is to continuously track the maximum power point of a PV module and adjust the operating condition to maximize the efficiency of a stand-alone water pumping system.
Which scientific methodology is employed?
The research uses mathematical modeling and computer simulation techniques, specifically utilizing MATLAB/SIMULINK to design and test the fuzzy logic controller.
What topics are discussed in the main body?
The main body covers the theoretical necessity of MPPT algorithms, the design steps of the fuzzy logic controller (fuzzification, inference, and defuzzification), and comparative simulation studies.
Which keywords best characterize this work?
Key terms include Photovoltaic (PV), Maximum Power Point Tracking (MPPT), Fuzzy Logic Control (FLC), MATLAB/SIMULINK, and DC-DC Converter.
Why is a Cúk converter used in this system?
A Cúk converter is utilized as the adaptation device between the PV source and the DC pump load to facilitate precise duty ratio control for MPPT.
How does the proposed system perform compared to a direct-coupled system?
Simulations show that the system with the fuzzy logic MPPT controller significantly increases the overall efficiency of energy production and improves water flow rates, especially during non-peak sun hours.
- Citation du texte
- Mohamed Ezzat Salem (Auteur), 2004, Maximum Power Point Tracking Using Fuzzy Logic Control, Munich, GRIN Verlag, https://www.grin.com/document/174104
