New Gen Controlling Variable Using Dragonfly Algorithm in PV Panel

Shabana Urooj, Fadwa Alrowais, Ramya Kuppusamy, Yuvaraja Teekaraman and Hariprasath Manoharan
Additional contact information
Shabana Urooj: Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi Arabia
Fadwa Alrowais: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi Arabia
Ramya Kuppusamy: Department of Electrical and Electronics Engineering, Sri Sairam College of Engineering, Bangalore City, Bengaluru 562106, India
Yuvaraja Teekaraman: MOBI- Mobility, Logistics and Automotive Technology Research Centre, EVERGi, Vrije Universiteit Brussels, 1050 Ixelles, Belgium
Hariprasath Manoharan: Department of Electronics and Communication Engineering, Audisankara College of Engineering and Technology, Gudur 524101, India

Energies, 2021, vol. 14, issue 4, 1-14

Abstract: In the present scenario the depletion of conventional sources causes an energy crisis. The energy crisis causes load demand with respect to electricity. The use of renewable energy sources plays a vital role in reducing the energy crisis and in reduction of CO 2 emission. The use of solar energy is the major source of power in generation as this is the root cause for the development of wind, tides, etc. However, due to climatic condition the availability of PV sources varies from time to time. Hence it is essential to track the maximum source of energy by implementing different types of MPPT algorithms. However, use of MPPT algorithms has the limitation of using the same during partial shadow conditions. The issue of tracking power under partial shadow conditions can be resolved by implementing an intelligent optimization tracking algorithm which involves a computation process. Though many of nature’s inspired algorithms were present to address real world problems, Mirjalili developed the dragonfly algorithm to provide a better optimization solution to the issues faced in real-time applications. The proposed concept focuses on the implementation of the dragonfly optimization algorithm to track the maximum power from solar and involves the concept of machine learning, image processing, and data computation.

Keywords: dragonfly optimization algorithm; maximum power tracker; PV panel (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/4/790/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/4/790/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:4:p:790-:d:492068

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().