MAXIMUM POWER POINT TRACKING FOR PHOTO MODULES

Authors

  • V. M. Sineglazov National Aviation University, Kyiv
  • D. P. Karabetsky National Aviation University, Kyiv
  • O. V. Klanovets National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

DOI:

https://doi.org/10.18372/1990-5548.63.14518

Keywords:

Maximum power point tracking, photo-voltaic modules, renewable energy sources, meta-algorithm, swarm intelligence algorithms, Bat Search algorithm, Particle Swarm Optimization

Abstract

It is considered the problem of maximum power point tracking for photovoltaic modules. The solution of this problem is based on a static parametric meta-optimization for swarm intelligence algorithms. For the problem solution Particle Swarm Optimization algorithm meta-optimized with Bat Search algorithm is proposed. The basic idea for the algorithms is that they consist of the base algorithm, which is used to solve the main optimization problem and the meta-algorithm used to find an optimal strategy for the base one. The object function for the base algorithm is an object function of the general optimization problem. The meta-algorithm evaluates the meta-function defined at each generation and tries to minimize it by searching for better strategies for the base algorithm. It is considered the example of its application.

Author Biographies

V. M. Sineglazov, National Aviation University, Kyiv

Aviation Computer-Integrated Complexes Department

Doctor of Engineering Science. Professor. Head of the Department

orcid.org/0000-0002-3297-9060

D. P. Karabetsky, National Aviation University, Kyiv

Aviation Computer-Integrated Complexes Department

Post-graduate student

O. V. Klanovets, National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Technical Cybernetic Department

Bachelor

References

V. M. Sineglazov, "Prospects for the development of the global electric power industry until 2035," Electricity, transmission and distribution. 2011, no. 2. p. 103.

C. Tugcu, I. Ozturk, and А. Alper, "Renewable and non-renewable energy consumption and economic growth relationship revisited: Evidence from G7 countries," Energy Economics, vol. 34, Issue 6, November 2012, 1942 р. https://doi.org/10.1016/j.eneco.2012.08.021

Javad Farzaneh, Reza Keypour, and Mojtaba Ahmadieh Khanesar, “A New Maximum Power Point Tracking Based on Modified Firefly Algorithm for PV System Under Partial Shading Conditions,” Technol Econ Smart Grids Sustain Energy, 2018, 3:9. https://doi.org/10.1007/S40866-018-0048-7

J. Wei, Y. Wang, and H. Wang, "A hybrid particle swarm evolutionary algorithm for constrained multi-objective optimization," Computing and Informatics, vol. 29, pp. 701–718, 2010.

Downloads

Issue

Vol. 1 No. 63 (2020)

Section

POWER MACHINERY

License

Authors who publish with this journal agree to the following terms:

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).