Version 1
: Received: 10 October 2024 / Approved: 10 October 2024 / Online: 10 October 2024 (12:01:59 CEST)
How to cite:
Han, J.; Lee, H.; Shon, J. Improvement of Power Production Efficiency Following the Application of the GD InC Maximum Power Point Tracking Method in Photovoltaic Systems. Preprints2024, 2024100774. https://doi.org/10.20944/preprints202410.0774.v1
Han, J.; Lee, H.; Shon, J. Improvement of Power Production Efficiency Following the Application of the GD InC Maximum Power Point Tracking Method in Photovoltaic Systems. Preprints 2024, 2024100774. https://doi.org/10.20944/preprints202410.0774.v1
Han, J.; Lee, H.; Shon, J. Improvement of Power Production Efficiency Following the Application of the GD InC Maximum Power Point Tracking Method in Photovoltaic Systems. Preprints2024, 2024100774. https://doi.org/10.20944/preprints202410.0774.v1
APA Style
Han, J., Lee, H., & Shon, J. (2024). Improvement of Power Production Efficiency Following the Application of the GD InC Maximum Power Point Tracking Method in Photovoltaic Systems. Preprints. https://doi.org/10.20944/preprints202410.0774.v1
Chicago/Turabian Style
Han, J., Hyunjae Lee and Jingeun Shon. 2024 "Improvement of Power Production Efficiency Following the Application of the GD InC Maximum Power Point Tracking Method in Photovoltaic Systems" Preprints. https://doi.org/10.20944/preprints202410.0774.v1
Abstract
This study proposes a new maximum power point tracking (MPPT) method based on machine learning with improved power production efficiency for application to photovoltaic (PV) systems. Power loss occurs in the incremental conductance (InC) method, depending on the size of the voltage step used to track the maximum power point. Additionally, the size of the voltage step must be specified by the initial user; however, an appropriate size cannot be determined in a rapidly changing environment. To solve this problem, this study presents a gradient descent InC (GD InC) method that optimizes the size of the voltage step by applying an optimization method based on machine learning. The effectiveness of the GD InC method was verified and the optimized size of the voltage step was confirmed to produce the largest amount of power. When the size of the voltage step was optimized, a maximum difference of 4.53% was observed compared to case when the smallest amount of power was produced. The effectiveness of the GD InC method, which improved the efficiency of power production by optimizing the size of the voltage step, was verified. Power can be produced efficiently by applying the GD InC method to PV systems.
Keywords
gradient descent; incremental conductance; machine learning; maximum power point tracking; photovoltaic
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
