Publication Date
2024
Document Type
Dissertation/Thesis
First Advisor
Shamim, Tariq
Degree Name
M.S. (Master of Science)
Legacy Department
Department of Mechanical Engineering
Abstract
The efficiency levels to convert from sunlight to electricity are in the range up to 22% commercially, based on the technology used. Due to comparatively low efficiency of photovoltaic systems, it is imperative to extract maximum power from the PV module array. Thus, in solar PV system, tracking the module’s MPP (maximum power point) is crucial and at the same time challenging as well due to varying climatic conditions. This research focuses on the modeling and performance evaluation of traditional Maximum Power Point Tracking (MPPT) techniques and AI-Based MPPT algorithms in photovoltaic systems. It addresses the issue of dynamic adjustment of the electrical operating point depending on various environmental conditions to maintain peak power output. The study utilizes a simulated PV cell and identifies suitable algorithms, comparing Perturb & Observe MPPT techniques to a machine learning based Neural Network algorithm which finds the most effective electrical point for the PV model in question. The study searched into various performance aspects such as power output, accuracy, stability, efficiency, and convergence time under different seasonal variations, including investigation of mathematical functions using both sinusoidal and step-change irradiance inputs. The ANN-based MPPT system consistently demonstrated superior performance across all parameters when compared to traditional P&O methods. It exhibited faster convergence to the maximum power point (MPP), reduced oscillations, and higher power output, especially in scenarios where environmental conditions changed rapidly. The study also discussed the process of integrating AI techniques into MPPT. The goal of the study is to conclude the suitable PV system analyzing the results and performance comparison between the two systems.
Recommended Citation
Badruddaza, Md, "Modeling and Performance Evaluation of Traditional and AI-Based MPPT Techniques for Photovoltaic Systems." (2024). Graduate Research Theses & Dissertations. 8008.
https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/8008
Extent
86 pages
Language
en
Publisher
Northern Illinois University
Rights Statement
In Copyright
Rights Statement 2
NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.
Media Type
Text
