| Title |
A Study on Increase Power Production in Photovoltaic Power Systems Applying the OS MPPT Method |
| Authors |
한정원(Jeong-Won Han) ; 이현재(Hyun-Jae Lee) ; 손진근(Jin-Geun Shon) |
| DOI |
https://doi.org/10.5370/KIEE.2024.73.9.1602 |
| Keywords |
Gradient Descent; InC (Incremental Conductance); Machine Learning; MPPT(Maximum Power Point Tracking); PV(Photovoltaic) |
| Abstract |
This paper, we conducted a study on methods for improving the power production efficiency of photovoltaic power systems using machine learning. The InC method, one of the MPPT methods used to improve the power production efficiency of photovoltaic power systems, determines the power production efficiency by the size of the slope, and to compensate for the disadvantages that occur in this process, we propose an OS MPPT (Optimized Slope MPPT) method that optimizes the constant value that α determines the size of the slope by introducing the optimization method of machine learning.
The effects of the OS MPPT method were analyzed through simulation experiments and field experiments. The results of comparing the calculated values by accumulating the produced power showed that the performance of the OS MPPT control method was effective. It is expected to contribute to the promotion of power production when applied to photovoltaic power systems. |