Research Article
BibTex RIS Cite

Optimization of Fuzzy Logic Controller with Genetic Algorithm for Maximum Power Point Tracking

Year 2021, Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special, 400 - 410, 20.10.2021
https://doi.org/10.53070/bbd.991349

Abstract

In this study, the optimization of the Fuzzy Logic Controller (FLC) for the process of tracking the maximum power point of the PV panel is discussed. In the study, the optimal values of the universal set intervals of the input and output variables of the FLC were determined by using Genetic Algorithm (GA) optimization. With the FLC designed with the universal set area obtained by GA optimization, the expected maximum power points for different irradiance values can be tracked successfully. The applications were carried out in the form of simulation study in Matlab-Simulink software.

References

  • Özdemir Ş. (2007), Fotovoltaik Sistemler İçin Mikrodenetleyicili En Yüksek Güç Noktasını İzleyen Bir Konvertörün Gerçekleştirilmesi, Yüksek Lisans Tezi, Elektrik Eğitimi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Erdoğan Y., Dinçler T., Kuncan M. , Ertunç H. M. (2014) Güneş Panelleri için Yüksek Verimli Maksimum Güç Noktası İzleyicisi (MPPT)Tasarımı, TOK 2014 Bildiriler Kitabı, pp.1055-1060.
  • Saleh Elkelani Babaa, Matthew Armstrong, Volker Pickert (2014), Overview of Maximum Power Point Tracking Control Methods for PV Systems, Journal of Power and Energy Engineering, 2: 59-72.
  • Mohamed E. El Telbany, Ayman Youssef, Abdelhalim Abdelnaby Zekry (2014), Intelligent Techniques for MPPT Control in Photovoltaic Systems: A Comprehensive Review, 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, 2014, pp.17-22.
  • Govind Anil, Nirmal Murugan, Mufeed Ubaid (2013), PI Controller based MPPT for a PV System, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 6(5): pp. 10-15.
  • Ali Reza Reisi, Mohammad Hassan Moradi, Shahriar Jamasb (2013), Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review, Renewable and Sustainable Energy Reviews, 19: 433-443.
  • Ahmed M. Fares, Belal A. Abo Zalam, Salwa G. El Nashar, Haitham Aka (2013), Comparison Between Different Algorithms for Maximum PPT in Photovoltaic Systems and its Implementation on Microcontroller, Journal of Energy Technologies and Policy, 3, (5)
  • Bounechbaa H., Bouzida A., Nabtib K., Benallab H. (2014), Comparison of perturb & observe and fuzzy logic in maximum power point tracker for PV systems, The International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES14, Energy Procedia 50 (2014), pp. 677 – 684.

Maksimum Yüksek Güç Noktası Takibi İçin Bulanık Mantık Denetleyicinin Genetik Algoritma ile Optimizasyonu

Year 2021, Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special, 400 - 410, 20.10.2021
https://doi.org/10.53070/bbd.991349

Abstract

Bu çalışmada, PV panelin maksimum güç noktasının takibi süreci için Bulanık Mantık Denetleyicinin (BMD) optimizasyonu konu alınmıştır. Çalışmada BMD giriş ve çıkış değişkenlerinin evrensel küme aralıklarının en uygun değerleri Genetik Algoritma (GA) optimizasyonu kullanılarak tespit edilmiştir. GA optimizasyonu ile elde edilen evrensel küme aralığı ile tasarlanan BMD ile farklı ışınım değerleri için beklenen maksimum güç noktalarının takibi başarı ile gerçekleştirilebilmiştir. Uygulamalar Matlab Simulink yazılımlarında benzetim çalışması şeklinde gerçekleştirilmiştir.

References

  • Özdemir Ş. (2007), Fotovoltaik Sistemler İçin Mikrodenetleyicili En Yüksek Güç Noktasını İzleyen Bir Konvertörün Gerçekleştirilmesi, Yüksek Lisans Tezi, Elektrik Eğitimi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Erdoğan Y., Dinçler T., Kuncan M. , Ertunç H. M. (2014) Güneş Panelleri için Yüksek Verimli Maksimum Güç Noktası İzleyicisi (MPPT)Tasarımı, TOK 2014 Bildiriler Kitabı, pp.1055-1060.
  • Saleh Elkelani Babaa, Matthew Armstrong, Volker Pickert (2014), Overview of Maximum Power Point Tracking Control Methods for PV Systems, Journal of Power and Energy Engineering, 2: 59-72.
  • Mohamed E. El Telbany, Ayman Youssef, Abdelhalim Abdelnaby Zekry (2014), Intelligent Techniques for MPPT Control in Photovoltaic Systems: A Comprehensive Review, 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, 2014, pp.17-22.
  • Govind Anil, Nirmal Murugan, Mufeed Ubaid (2013), PI Controller based MPPT for a PV System, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 6(5): pp. 10-15.
  • Ali Reza Reisi, Mohammad Hassan Moradi, Shahriar Jamasb (2013), Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review, Renewable and Sustainable Energy Reviews, 19: 433-443.
  • Ahmed M. Fares, Belal A. Abo Zalam, Salwa G. El Nashar, Haitham Aka (2013), Comparison Between Different Algorithms for Maximum PPT in Photovoltaic Systems and its Implementation on Microcontroller, Journal of Energy Technologies and Policy, 3, (5)
  • Bounechbaa H., Bouzida A., Nabtib K., Benallab H. (2014), Comparison of perturb & observe and fuzzy logic in maximum power point tracker for PV systems, The International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES14, Energy Procedia 50 (2014), pp. 677 – 684.
There are 8 citations in total.

Details

Primary Language Turkish
Subjects Software Engineering (Other)
Journal Section PAPERS
Authors

Mehmet Serhat Can 0000-0003-2356-9921

Ömerülfaruk Özgüven

Publication Date October 20, 2021
Submission Date September 5, 2021
Acceptance Date September 21, 2021
Published in Issue Year 2021 Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special

Cite

APA Can, M. S., & Özgüven, Ö. (2021). Maksimum Yüksek Güç Noktası Takibi İçin Bulanık Mantık Denetleyicinin Genetik Algoritma ile Optimizasyonu. Computer Science, IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium(Special), 400-410. https://doi.org/10.53070/bbd.991349

The Creative Commons Attribution 4.0 International License 88x31.png is applied to all research papers published by JCS and

A Digital Object Identifier (DOI) Logo_TM.png is assigned for each published paper