A Novel Algorithm-based MPPT Strategy for PV Power Systems under Partial Shading Conditions
DOI:
https://doi.org/10.5755/j02.eie.30183Keywords:
DC-DC boost converter, Short-distance running algorithm (SDRA), Partial shading conditions, MPP tracking, Photovoltaic (PV) powerAbstract
This paper proposes a new simple method based on the simulation of a short-distance running race in athletics to track the maximum power point (MPP) for photovoltaic (PV) power systems, which can improve the tracking speed and search accuracy. In detail, the DC-DC boost converter is utilized to transfer the power of PV panels to a load and follows the MPP all the time, regardless of the environmental temperature and variant solar irradiance. In the MPP search method based on the proposed short-distance running algorithm (SDRA), an appropriate duty ratio value will be found so that the load receives the highest electrical power from the PV energy system. As a result, the SDRA method has excellent MPP tracking ability with high convergence speed and no oscillation. The efficiency of the proposed method is verified by simulation and experiments. The proposed SDRA method overcomes the obstruction of local traps to achieve global MPP. The results have shown that the SDRA method has advantages in terms of better convergence speed and performance than the particle swarm optimization (PSO) and grey wolf algorithm (GWA) methods when they are operated under the same conditions.
Downloads
Published
How to Cite
Issue
Section
License
The copyright for the paper in this journal is retained by the author(s) with the first publication right granted to the journal. The authors agree to the Creative Commons Attribution 4.0 (CC BY 4.0) agreement under which the paper in the Journal is licensed.
By virtue of their appearance in this open access journal, papers are free to use with proper attribution in educational and other non-commercial settings with an acknowledgement of the initial publication in the journal.
Funding data
-
Ministry of Science and Technology of the People's Republic of China
Grant numbers MOST-107-2221-E-033- 064