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Comparison of The Perturb & Observe, Increased Conductivity and Particle Swarm Optimization Algorithms for Maximum Power Point Tracking in Photovoltaic Systems

Year 2021, Volume 13, Issue 3, 202 - 214, 31.12.2021
https://doi.org/10.29137/umagd.997229

Abstract

With the increasing world population, the interest in renewable energy sources has increased in order to reduce the decrease in petroleum and its derivatives products used for energy production, and to minimize the damage caused by gases such as carbon monoxide and methane gas, which are produced as waste from these products. Examples of renewable energy sources are wind, fuel cell and solar panels. In the article study, in solar panel systems, Maximum Power Point Tracking (MPPT) with Direct Current (DC) converter and The Perturb & Observe (P&O), Increasing Conductivity and Particle Swarm Optimization (PSO) algorithms were designed in MATLAB/Simulink environment and simulation studies at variable radiation values has been carried out. As a result of the simulation studies, it has been observed that the PSO MPPT algorithm oscillates less at the variable radiation values at the maximum power point and reaches the maximum power point faster than the P&O and Increasing Conductivity algorithms.

References

  • Darwesh, M. R., & Ghoname, M. S. (2021). Experimental studies on the contribution of solar energy as a source for heating biogas digestion units. Energy Reports, 7, 1657-1671.
  • Divyasharon, R., Banu, R. N., & Devaraj, D. (2019). Artificial neural network based MPPT with CUK converter topology for PV systems under varying climatic conditions. In 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) (pp. 1-6). IEEE.
  • Doubabi, H., Salhi, I., Chennani, M., & Essounbouli, N. (2021). High Performance MPPT based on TS Fuzzy–integral backstepping control for PV system under rapid varying irradiance—Experimental validation. ISA transactions.
  • Fathi, M., & Parian, J. A. (2021). Intelligent MPPT for photovoltaic panels using a novel fuzzy logic and artificial neural networks based on evolutionary algorithms. Energy Reports, 7, 1338-1348.
  • Gümüş, Z., & Demirtaş, M. (2021). Fotovoltaik Sistemlerde Maksimum Güç Noktası Takibinde Kullanılan Algoritmaların Kısmi Gölgeleme Koşulları Altında Karşılaştırılması. Politeknik Dergisi, 1-1.
  • Güngör, O. (2019). Güneş Panellerinde Cuk Dönüştürücü Tabanlı Değişken Şartlar Altında PNO, BM ve YSA Algoritmalarının Karşılaştırmalı Performans Analizi. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 6(1), 66-76.
  • Hamidon, F. Z., Aziz, P. A., & Yunus, N. M. (2012). Photovoltaic array modelling with P&O MPPT algorithm in MATLAB. In 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE) (pp. 1-5). IEEE.
  • Harrag, A., Messalti, S., & Daili, Y. (2019). Innovative single sensor neural network PV MPPT. In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 1895-1899). IEEE.
  • Keskin, Y. E., Başoğlu, M. E., Tekdemir, İ. G., & Çakır, B. (2014). Fotovoltaik Sistemlerde D&G ve Artan İletkenlik Algoritmalarının Karşılaştırılması Comparison of P&O and Incremental Conductance Algorithms for Photovoltaic Systems.
  • Kollimalla, S. K., & Mishra, M. K. (2013). Novel adaptive P&O MPPT algorithm for photovoltaic system considering sudden changes in weather condition. In 2013 International Conference on Clean Electrical Power (ICCEP) (pp. 653-658). IEEE.
  • Kumar, V., Ghosh, S., Naidu, N. S., Kamal, S., Saket, R. K., & Nagar, S. K. (2021). Load voltage-based MPPT technique for standalone PV systems using adaptive step. International Journal of Electrical Power & Energy Systems, 128, 106732.
  • Laxman, B., Annamraju, A., & Srikanth, N. V. (2021). A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids. International Journal of Hydrogen Energy, 46(18), 10653-10665.
  • Mahapatra, S., Badi, M., & Raj, S. (2019). Implementation of PSO, it’s variants and Hybrid GWO-PSO for improving Reactive Power Planning. In 2019 Global Conference for Advancement in Technology (GCAT) (pp. 1-6). IEEE.
  • Nazir, M. S., Abdalla, A. N., Wang, Y., Chu, Z., Jie, J., Tian, P., ... & Tang, Y. (2020). Optimization configuration of energy storage capacity based on the microgrid reliable output power. Journal of Energy Storage, 32, 101866.
  • Patel, A., & Tiwari, H. (2017). Implementation of INC-PIMPPT and its comparison with INC MPPT by direct duty cycle control for solar photovoltaics employing zeta converter. In 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC) (pp. 1-6). IEEE.
  • Pradhan, A., & Panda, B. (2018). A simplified design and modeling of boost converter for photovoltaic sytem. International Journal of Electrical and Computer Engineering, 8(1), 141.
  • Selmi, T., Abdul-Niby, M., Devis, L., & Davis, A. (2014). P&O mppt implementation using matlab/simulink. In 2014 Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER) (pp. 1-4). IEEE.
  • Sharif, A., Meo, M. S., Chowdhury, M. A. F., & Sohag, K. (2021). Role of solar energy in reducing ecological footprints: An empirical analysis. Journal of Cleaner Production, 292, 126028.
  • Sher, H. A., Murtaza, A. F., Noman, A., Addoweesh, K. E., Al-Haddad, K., & Chiaberge, M. (2015). A new sensorless hybrid MPPT algorithm based on fractional short-circuit current measurement and P&O MPPT. IEEE Transactions on sustainable energy, 6(4), 1426-1434.
  • Shi, J., Zhang, W., Zhang, Y., Xue, F., & Yang, T. (2015). MPPT for PV systems based on a dormant PSO algorithm. Electric Power Systems Research, 123, 100-107.
  • Toylan, H., & HÜNER, E. (2017). Uyarlamalı Sinirsel Bulanık Çıkarım (ANFIS) Tabanlı Güneş Takip Sistemi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17(2), 546-554.
  • Turgay, K. A. Y. A., & GÜLER, H. (2016) Güneş Takip Sistemlerinde Maksimum Çıkış Gerilimi için Bulanık-Genetik Algoritma Tabanlı Sistem Tasarımı. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 99-108.
  • Yıldızay, H. D., Haydar, A. R. A. S., & YILMAZ, V. (2014). Eskişehir’de rüzgâr ve güneş enerjisi potansiyelinin belirlenmesi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 5(1), 49-58.
  • Zafar, M. H., Khan, N. M., Mirza, A. F., Mansoor, M., Akhtar, N., Qadir, M. U., ... & Moosavi, S. K. R. (2021). A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition. Sustainable Energy Technologies and Assessments, 47, 101367.

Fotovoltaik Sistemlerde Maksimum Güç Noktası Takibi İçin Değiştir – Gözle, Artan İletkenlik Ve Parçacık Sürü Optimizasyon Algoritmalarının Karşılaştırılması

Year 2021, Volume 13, Issue 3, 202 - 214, 31.12.2021
https://doi.org/10.29137/umagd.997229

Abstract

Artan dünya nüfusuyla birlikte enerji üretimek amacıyla kullanılan petrol ve türevleri ürünlerin azalması, bu ürünlerden atık olarak ortaya çıkan karbon monoksit, metan gazı gibi gazların çevreye verdiği zararı minimum değere indirgemek amacıyla yenilebilir enerji kaynaklarına ilgi artmıştır. Yenilenebilir enerji kaynakları rüzgâr, yakıt hücresi ve güneş panelleri gibi örneklendirilebilir. Yapılan makale çalışmasında güneş paneli sistemlerinde, Doğru Akım (DA) dönüştürücüsü ve Değiştir- Gözle (D&G), Artan İletkenlik ve Parçacık Sürü Optimizasyonu (PSO) algoritmaları ile Maksimum Güç Noktası Takibi (MGNT) MATLAB/Simulink ortamında tasarlanmış ve değişken ışıma değerlerinde benzetim çalışmaları gerçekleştirilmiştir. Benzetim çalışmaları sonucunda PSO MGNT algoritmasının, D&G ve Artan İletkenlik algoritmasına göre maksimum güç noktasında değişken ışınım değerlerinde daha az salınım yapmakta olduğu ve maksimumum güç noktasına daha hızlı ulaştığı gözlemlenmiştir.

References

  • Darwesh, M. R., & Ghoname, M. S. (2021). Experimental studies on the contribution of solar energy as a source for heating biogas digestion units. Energy Reports, 7, 1657-1671.
  • Divyasharon, R., Banu, R. N., & Devaraj, D. (2019). Artificial neural network based MPPT with CUK converter topology for PV systems under varying climatic conditions. In 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) (pp. 1-6). IEEE.
  • Doubabi, H., Salhi, I., Chennani, M., & Essounbouli, N. (2021). High Performance MPPT based on TS Fuzzy–integral backstepping control for PV system under rapid varying irradiance—Experimental validation. ISA transactions.
  • Fathi, M., & Parian, J. A. (2021). Intelligent MPPT for photovoltaic panels using a novel fuzzy logic and artificial neural networks based on evolutionary algorithms. Energy Reports, 7, 1338-1348.
  • Gümüş, Z., & Demirtaş, M. (2021). Fotovoltaik Sistemlerde Maksimum Güç Noktası Takibinde Kullanılan Algoritmaların Kısmi Gölgeleme Koşulları Altında Karşılaştırılması. Politeknik Dergisi, 1-1.
  • Güngör, O. (2019). Güneş Panellerinde Cuk Dönüştürücü Tabanlı Değişken Şartlar Altında PNO, BM ve YSA Algoritmalarının Karşılaştırmalı Performans Analizi. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 6(1), 66-76.
  • Hamidon, F. Z., Aziz, P. A., & Yunus, N. M. (2012). Photovoltaic array modelling with P&O MPPT algorithm in MATLAB. In 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE) (pp. 1-5). IEEE.
  • Harrag, A., Messalti, S., & Daili, Y. (2019). Innovative single sensor neural network PV MPPT. In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 1895-1899). IEEE.
  • Keskin, Y. E., Başoğlu, M. E., Tekdemir, İ. G., & Çakır, B. (2014). Fotovoltaik Sistemlerde D&G ve Artan İletkenlik Algoritmalarının Karşılaştırılması Comparison of P&O and Incremental Conductance Algorithms for Photovoltaic Systems.
  • Kollimalla, S. K., & Mishra, M. K. (2013). Novel adaptive P&O MPPT algorithm for photovoltaic system considering sudden changes in weather condition. In 2013 International Conference on Clean Electrical Power (ICCEP) (pp. 653-658). IEEE.
  • Kumar, V., Ghosh, S., Naidu, N. S., Kamal, S., Saket, R. K., & Nagar, S. K. (2021). Load voltage-based MPPT technique for standalone PV systems using adaptive step. International Journal of Electrical Power & Energy Systems, 128, 106732.
  • Laxman, B., Annamraju, A., & Srikanth, N. V. (2021). A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids. International Journal of Hydrogen Energy, 46(18), 10653-10665.
  • Mahapatra, S., Badi, M., & Raj, S. (2019). Implementation of PSO, it’s variants and Hybrid GWO-PSO for improving Reactive Power Planning. In 2019 Global Conference for Advancement in Technology (GCAT) (pp. 1-6). IEEE.
  • Nazir, M. S., Abdalla, A. N., Wang, Y., Chu, Z., Jie, J., Tian, P., ... & Tang, Y. (2020). Optimization configuration of energy storage capacity based on the microgrid reliable output power. Journal of Energy Storage, 32, 101866.
  • Patel, A., & Tiwari, H. (2017). Implementation of INC-PIMPPT and its comparison with INC MPPT by direct duty cycle control for solar photovoltaics employing zeta converter. In 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC) (pp. 1-6). IEEE.
  • Pradhan, A., & Panda, B. (2018). A simplified design and modeling of boost converter for photovoltaic sytem. International Journal of Electrical and Computer Engineering, 8(1), 141.
  • Selmi, T., Abdul-Niby, M., Devis, L., & Davis, A. (2014). P&O mppt implementation using matlab/simulink. In 2014 Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER) (pp. 1-4). IEEE.
  • Sharif, A., Meo, M. S., Chowdhury, M. A. F., & Sohag, K. (2021). Role of solar energy in reducing ecological footprints: An empirical analysis. Journal of Cleaner Production, 292, 126028.
  • Sher, H. A., Murtaza, A. F., Noman, A., Addoweesh, K. E., Al-Haddad, K., & Chiaberge, M. (2015). A new sensorless hybrid MPPT algorithm based on fractional short-circuit current measurement and P&O MPPT. IEEE Transactions on sustainable energy, 6(4), 1426-1434.
  • Shi, J., Zhang, W., Zhang, Y., Xue, F., & Yang, T. (2015). MPPT for PV systems based on a dormant PSO algorithm. Electric Power Systems Research, 123, 100-107.
  • Toylan, H., & HÜNER, E. (2017). Uyarlamalı Sinirsel Bulanık Çıkarım (ANFIS) Tabanlı Güneş Takip Sistemi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17(2), 546-554.
  • Turgay, K. A. Y. A., & GÜLER, H. (2016) Güneş Takip Sistemlerinde Maksimum Çıkış Gerilimi için Bulanık-Genetik Algoritma Tabanlı Sistem Tasarımı. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 99-108.
  • Yıldızay, H. D., Haydar, A. R. A. S., & YILMAZ, V. (2014). Eskişehir’de rüzgâr ve güneş enerjisi potansiyelinin belirlenmesi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 5(1), 49-58.
  • Zafar, M. H., Khan, N. M., Mirza, A. F., Mansoor, M., Akhtar, N., Qadir, M. U., ... & Moosavi, S. K. R. (2021). A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition. Sustainable Energy Technologies and Assessments, 47, 101367.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Murat LÜY
KIRIKKALE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ
0000-0002-2378-0009
Türkiye


Fuat TÜRK
TC MİLLİ EĞİTİM BAKANLIĞI NECATİBEY
0000-0001-8159-360X
Türkiye


Nuri Alper METİN (Primary Author)
Osmaniye Korkut Ata Üniversitesi
0000-0002-9962-917X
Türkiye

Publication Date December 31, 2021
Published in Issue Year 2021, Volume 13, Issue 3

Cite

APA Lüy, M. , Türk, F. & Metin, N. A. (2021). Fotovoltaik Sistemlerde Maksimum Güç Noktası Takibi İçin Değiştir – Gözle, Artan İletkenlik Ve Parçacık Sürü Optimizasyon Algoritmalarının Karşılaştırılması . International Journal of Engineering Research and Development , December 2021 Special Issue , 202-214 . DOI: 10.29137/umagd.997229

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