Maximum power point tracking for a pv system using tuned support vector regression by particle swarm optimization
Author
Source
Journal of Engineering Research
Issue
Vol. 8, Issue 4 (31 Dec. 2020), pp.139-152, 14 p.
Publisher
Kuwait University Academic Publication Council
Publication Date
2020-12-31
Country of Publication
Kuwait
No. of Pages
14
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Abstract EN
The photovoltaic (PV) system is always operated at the maximum power point (MPP) condition irrespective of the fluctuations in PV voltage.
The maximum power point tracking (MPPT) employed in PV system is not effective during the presence of current ripple as normal tracking becomes increasingly complex during fluctuation in solar irradiation or due to change in MPP condition.
This paper proposes a high-efficiency power point tracking algorithm to minimize the current ripple and power oscillation around the maximum power point.
The developed algorithm is based on particle swarm optimization-support vector regression (PSO-SVR) technique.
The proposed algorithm is implemented to select and tune the Support Vector Regression (SVR) parameters such as kernel parameters, variance, and the penalty factor for predicting the irradiation level as well as to determine the PV voltage corresponding of maximum power point.
The PSO method is used to accelerate the process of optimizing the SVR parameters at different conditions and get knowledge about the corresponding global optimum.
From the experimental results, the efficiency of maximum power point tracking is found to be 99.8%.
The proposed algorithm PSO-SVR shows a better performance than using SVR alone.
The stability and accuracy of MPPT have been validated during the rapid fluctuation of solar irradiation in the range of 25% to 100%.
American Psychological Association (APA)
Abu Khalil, Ahmad G.. 2020. Maximum power point tracking for a pv system using tuned support vector regression by particle swarm optimization. Journal of Engineering Research،Vol. 8, no. 4, pp.139-152.
https://search.emarefa.net/detail/BIM-1494677
Modern Language Association (MLA)
Abu Khalil, Ahmad G.. Maximum power point tracking for a pv system using tuned support vector regression by particle swarm optimization. Journal of Engineering Research Vol. 8, no. 4 (Dec. 2020), pp.139-152.
https://search.emarefa.net/detail/BIM-1494677
American Medical Association (AMA)
Abu Khalil, Ahmad G.. Maximum power point tracking for a pv system using tuned support vector regression by particle swarm optimization. Journal of Engineering Research. 2020. Vol. 8, no. 4, pp.139-152.
https://search.emarefa.net/detail/BIM-1494677
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 151-152
Record ID
BIM-1494677