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PARÇALI GÖLGELENME DURUMUNDA YAPAY SİNİR AĞLARI VE PARÇACIK SÜRÜ OPTİMİZASYONU TABANLI BİR MAKSİMUM GÜÇ NOKTASI TAKİBİ ALGORİTMASI

Yıl 2023, Cilt: 26 Sayı: 4, 895 - 908, 03.12.2023

Elif Baldan , Hüseyin Erişti

https://doi.org/10.17780/ksujes.1318480

Öz

Fotovoltaik (photovoltaic - PV) sistemlerde maksimum güç noktası takibi (MGNT) yapılırken gerçek koşullarda parçalı gölgelenme durumu oluşmaktadır. Bu makalede parçalı gölgelenme koşullarını incelemek için MATLAB/Simulink’te PV paneller ve yükseltici dönüştürücüden oluşan bir PV sistem oluşturulmuştur. Geleneksel ve yapay zeka tabanlı MGNT algoritmaları bu sistem üzerinde uygulanmıştır. Maksimum güç noktasını (MGN) takip etmek için geleneksel yöntem olan Değiştir ve Gözle algoritması ve Yapay Sinir Ağları (YSA) tekniği kullanılmıştır. Klasik YSA tekniğinin yanısıra Parçacık Sürü Optimizasyonu (PSO) ile hibrit bir teknik oluşturulmuştur Farklı senaryolar ile ilk olarak parçalı gölgelenme durumu simulasyon olarak oluşturulmuştur. Algoritmaların doğruluğunu desteklemek için hem güneşli hem de bulutlu olmak üzere iki güne ait gerçek zamanlı ışınım verileri toplanarak MATLAB/Simulink’te oluşturulan PV sistemde analizler yapılmıştır. Yapılan analizler sonucunda PSO tabanlı YSA tekniği diğer algoritmalara göre daha verimli bir şekilde MGN’yi izlediği gözlemlenmiştir. Bu çalışma ile parçalı gölgelenme durumunda MGNT üzerine yapılan çalışmalara katkı sağlanmaktadır ve yapay zeka algoritmalarının farklı bir alan olan PV sistemler için kullanımı gösterilmiştir.

Anahtar Kelimeler

Fotovoltaik panel değiştir ve gözle metotu parçalı gölgelenme yapay sinir ağları parçacık sürü optimizasyonu

Kaynakça

  • Ahmed, Sajib, Saad Mekhilef, Marizan Mubin, Kok Soon Tey, and Mostefa Kermadi. 2023. “An Enhanced Scanning Technique for Flexible Power Point Tracking under Partial Shading Condition.” Solar Energy 262(January):111817. doi: 10.1016/j.solener.2023.111817.
  • Al-Majidi, Sadeq D., Maysam F. Abbod, and Hamed S. Al-Raweshidy. 2020. “A Particle Swarm Optimisation-Trained Feedforward Neural Network for Predicting the Maximum Power Point of a Photovoltaic Array.” Engineering Applications of Artificial Intelligence 92(September 2019):103688. doi: 10.1016/j.engappai.2020.103688.
  • Bollipo, Ratnakar Babu, Suresh Mikkili, and Praveen Kumar Bonthagorla. 2020. “Hybrid, Optimization, Intelligent and Classical PV MPPT Techniques: Review.” CSEE Journal of Power and Energy Systems 7(1):9–33. doi: 10.17775/CSEEJPES.2019.02720.
  • Bouselham, L., B. Hajji, and H. Hajji. 2015. “Comparative Study of Different MPPT Methods for Photovoltaic System.” Pp. 1–5 in 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC). IEEE.
  • Divyasharon, R., R. Narmatha Banu, and D. Devaraj. 2019. “Artificial Neural Network Based MPPT with CUK Converter Topology for PV Systems Under Varying Climatic Conditions.” Pp. 1–6 in 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE.
  • Elbarbary, Zakaria Mohamed Salem, and Mohamed Abdullrahman Alranini. 2021. “Review of Maximum Power Point Tracking Algorithms of PV System.” Frontiers in Engineering and Built Environment 1(1):68–80. doi: 10.1108/FEBE-03-2021-0019.
  • Farah, Lotfi, Amir Hussain, Abdelfateh Kerrouche, Cosimo Ieracitano, Jamil Ahmad, and Mufti Mahmud. 2020. “A Highly-Efficient Fuzzy-Based Controller with High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System.” IEEE Access 8:163225–37. doi: 10.1109/ACCESS.2020.3016981.
  • Fathi, Milad, and Jafar Amiri Parian. 2021. “Intelligent MPPT for Photovoltaic Panels Using a Novel Fuzzy Logic and Artificial Neural Networks Based on Evolutionary Algorithms.” Energy Reports 7:1338–48. doi: 10.1016/j.egyr.2021.02.051.
  • Hashim, Hadi Fakhir, Marwah M. Kareem, Waleed Khalid Al-Azzawi, and Adnan H. Ali. 2021. “Improving the Performance of Photovoltaic Module during Partial Shading Using ANN.” International Journal of Power Electronics and Drive Systems (IJPEDS) 12(4):2435. doi: 10.11591/ijpeds.v12.i4.pp2435-2442.
  • Ibrahim, Al-wesabi Wesabi, M. B. B. Shafik, Min Ding, Mohammad Abu Sarhan, Zhijian Fang, Ahmed G. Alareqi, Tariq Almoqri, and Ayman M. Al-Rassas. 2020. “PV Maximum Power-Point Tracking Using Modified Particle Swarm Optimization under Partial Shading Conditions.” Chinese Journal of Electrical Engineering 6(4):106–21. doi: 10.23919/CJEE.2020.000035.
  • Javed, Saba, and Kashif Ishaque. 2022. “A Comprehensive Analyses with New Findings of Different PSO Variants for MPPT Problem under Partial Shading.” Ain Shams Engineering Journal 13(5):101680. doi: 10.1016/j.asej.2021.101680.
  • Li, Hong, Duo Yang, Wenzhe Su, Jinhu Lu, and Xinghuo Yu. 2019. “An Overall Distribution Particle Swarm Optimization MPPT Algorithm for Photovoltaic System Under Partial Shading.” IEEE Transactions on Industrial Electronics 66(1):265–75. doi: 10.1109/TIE.2018.2829668.
  • Majeed Shaikh, Abdul, Mohammad Fawad Shaikh, Shoaib Ahmed Shaikh, Moez Krichen, Rehan Ali Rahimoon, and Abdul Qadir. 2023. “Comparative Analysis of Different MPPT Techniques Using Boost Converter for Photovoltaic Systems under Dynamic Shading Conditions.” Sustainable Energy Technologies and Assessments 57(August 2022):103259. doi: 10.1016/j.seta.2023.103259.
  • Mao, Mingxuan, Lichuang Cui, Qianjin Zhang, Ke Guo, Lin Zhou, and Han Huang. 2020. “Classification and Summarization of Solar Photovoltaic MPPT Techniques: A Review Based on Traditional and Intelligent Control Strategies.” Energy Reports 6:1312–27. doi: 10.1016/j.egyr.2020.05.013.
  • Mittal, Poornima, Tarush Goel, and Pratyush Gupta. 2020. “Evolution of MPPT Algorithms in Solar Arrays.” Materials Today: Proceedings 37(Part 2):3154–58. doi: 10.1016/j.matpr.2020.09.045.
  • Miyatake, Masafumi, Fuhito Toriumi, Tsugio Endo, and Nobuhiko Fujii. 2007. “A Novel Maximum Power Point Tracker Controlling Several Converters Connected to Photovoltaic Arrays with Particle Swarm Optimization Technique.” Pp. 1–10 in 2007 European Conference on Power Electronics and Applications. IEEE.
  • Mountassir, Salaheddine, Saad Sarih, and Abdelouahed Tajer. 2022. “A FUZZY LOGIC MPPT BASED CONTROL FOR A PHOTOVOLTAIC SYSTEM.” Journal of Theoretical and Applied Information Technology 100(11):3730–38.
  • Obukhov, Sergey, Ahmed Ibrahim, Ahmed A. Zaki Diab, Ameena Saad Al-Sumaiti, and Raef Aboelsaud. 2020. “Optimal Performance of Dynamic Particle Swarm Optimization Based Maximum Power Trackers for Stand-Alone PV System Under Partial Shading Conditions.” IEEE Access 8:20770–85. doi: 10.1109/ACCESS.2020.2966430.
  • Pragallapati, Nataraj, Tanuj Sen, and Vivek Agarwal. 2017. “Adaptive Velocity PSO for Global Maximum Power Control of a PV Array Under Nonuniform Irradiation Conditions.” IEEE Journal of Photovoltaics 7(2):624–39. doi: 10.1109/JPHOTOV.2016.2629844.
  • Priyadarshi, Neeraj, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen, Frede Blaabjerg, and Mahajan Sagar Bhaskar. 2020. “An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration Under Fluctuating Sun Irradiance.” IEEE Systems Journal 14(1):1218–29. doi: 10.1109/JSYST.2019.2949083.
  • Rahman, Md. Motakabbir, and Md. Shahidul Islam. 2020. “PSO and ANN Based Hybrid MPPT Algorithm for Photovoltaic Array under Partial Shading Condition.” Engineering International 8(1):9–24. doi: 10.18034/ei.v8i1.481.
  • Rastogi, Digant, Manika Jain, and Mini Sreejeth. 2022. “Comparative Study of DC-DC Converters in PV Systems Using Fuzzy Logic MPPT Algorithm.” Pp. 1–7 in 2022 IEEE Delhi Section Conference (DELCON). IEEE.
  • Sai, Boni Satya Varun, Sarang A. Khadtare, and Debashis Chatterjee. 2023. “An Improved Weather Adaptable P&O MPPT Technique under Varying Irradiation Condition.” ISA Transactions (xxxx). doi: 10.1016/j.isatra.2023.05.025.
  • Sarvi, Mohammad, and Ahmad Azadian. 2022. “A Comprehensive Review and Classified Comparison of MPPT Algorithms in PV Systems.” Energy Systems 13(2):281–320. doi: 10.1007/s12667-021-00427-x.
  • Wasim, Muhammad Shahid, Muhammad Amjad, Salman Habib, Muhammad Abbas Abbasi, Abdul Rauf Bhatti, and S. M. Muyeen. 2022. “A Critical Review and Performance Comparisons of Swarm-Based Optimization Algorithms in Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions.” Energy Reports 8:4871–98. doi: 10.1016/j.egyr.2022.03.175.
  • Zhang, Wei, Guopeng Zhou, Hao Ni, and Yunlian Sun. 2019. “A Modified Hybrid Maximum Power Point Tracking Method for Photovoltaic Arrays Under Partially Shading Condition.” IEEE Access 7:160091–100. doi: 10.1109/ACCESS.2019.2950375.

A MAXIMUM POWER POINT TRACKING ALGORITHM BASED ON ARTIFICAL NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION IN PARTAL SHADING

Yıl 2023, Cilt: 26 Sayı: 4, 895 - 908, 03.12.2023

Elif Baldan , Hüseyin Erişti

https://doi.org/10.17780/ksujes.1318480

Öz

In photovoltaic (PV) systems, partial shading occurs under real conditions when maximum power point tracking (MPPT) is performed. In this paper, a PV system consisting of PV panels and a boost converter is created in MATLAB/Simulink to investigate the partial shadowing conditions. Conventional and artificial intelligence-based MGNT algorithms are applied to this system. In order to track the maximum power point (MPP), the traditional method of the Perturb and Observe algorithm and Artificial Neural Networks (ANN) technique are used. In addition to the classical ANN technique, a hybrid technique was created with Particle Swarm Optimization (PSO). First, the partial shading situation was simulated with different scenarios. To support the accuracy of the algorithms, real-time irradiance data for two days, both sunny and cloudy, were collected and analyzed in MATLAB/Simulink on the PV system. As a result of the analysis, it was observed that the PSO-based ANN technique tracks MPP more efficiently than other algorithms. This study contributes to the studies on MGNT in the case of partial shading and demonstrates the use of artificial intelligence algorithms for PV systems, which is a different field.

Anahtar Kelimeler

Photovoltaic panel perturb and observe partial shading artificial neural network partical swarm optimization

Kaynakça

  • Ahmed, Sajib, Saad Mekhilef, Marizan Mubin, Kok Soon Tey, and Mostefa Kermadi. 2023. “An Enhanced Scanning Technique for Flexible Power Point Tracking under Partial Shading Condition.” Solar Energy 262(January):111817. doi: 10.1016/j.solener.2023.111817.
  • Al-Majidi, Sadeq D., Maysam F. Abbod, and Hamed S. Al-Raweshidy. 2020. “A Particle Swarm Optimisation-Trained Feedforward Neural Network for Predicting the Maximum Power Point of a Photovoltaic Array.” Engineering Applications of Artificial Intelligence 92(September 2019):103688. doi: 10.1016/j.engappai.2020.103688.
  • Bollipo, Ratnakar Babu, Suresh Mikkili, and Praveen Kumar Bonthagorla. 2020. “Hybrid, Optimization, Intelligent and Classical PV MPPT Techniques: Review.” CSEE Journal of Power and Energy Systems 7(1):9–33. doi: 10.17775/CSEEJPES.2019.02720.
  • Bouselham, L., B. Hajji, and H. Hajji. 2015. “Comparative Study of Different MPPT Methods for Photovoltaic System.” Pp. 1–5 in 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC). IEEE.
  • Divyasharon, R., R. Narmatha Banu, and D. Devaraj. 2019. “Artificial Neural Network Based MPPT with CUK Converter Topology for PV Systems Under Varying Climatic Conditions.” Pp. 1–6 in 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE.
  • Elbarbary, Zakaria Mohamed Salem, and Mohamed Abdullrahman Alranini. 2021. “Review of Maximum Power Point Tracking Algorithms of PV System.” Frontiers in Engineering and Built Environment 1(1):68–80. doi: 10.1108/FEBE-03-2021-0019.
  • Farah, Lotfi, Amir Hussain, Abdelfateh Kerrouche, Cosimo Ieracitano, Jamil Ahmad, and Mufti Mahmud. 2020. “A Highly-Efficient Fuzzy-Based Controller with High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System.” IEEE Access 8:163225–37. doi: 10.1109/ACCESS.2020.3016981.
  • Fathi, Milad, and Jafar Amiri Parian. 2021. “Intelligent MPPT for Photovoltaic Panels Using a Novel Fuzzy Logic and Artificial Neural Networks Based on Evolutionary Algorithms.” Energy Reports 7:1338–48. doi: 10.1016/j.egyr.2021.02.051.
  • Hashim, Hadi Fakhir, Marwah M. Kareem, Waleed Khalid Al-Azzawi, and Adnan H. Ali. 2021. “Improving the Performance of Photovoltaic Module during Partial Shading Using ANN.” International Journal of Power Electronics and Drive Systems (IJPEDS) 12(4):2435. doi: 10.11591/ijpeds.v12.i4.pp2435-2442.
  • Ibrahim, Al-wesabi Wesabi, M. B. B. Shafik, Min Ding, Mohammad Abu Sarhan, Zhijian Fang, Ahmed G. Alareqi, Tariq Almoqri, and Ayman M. Al-Rassas. 2020. “PV Maximum Power-Point Tracking Using Modified Particle Swarm Optimization under Partial Shading Conditions.” Chinese Journal of Electrical Engineering 6(4):106–21. doi: 10.23919/CJEE.2020.000035.
  • Javed, Saba, and Kashif Ishaque. 2022. “A Comprehensive Analyses with New Findings of Different PSO Variants for MPPT Problem under Partial Shading.” Ain Shams Engineering Journal 13(5):101680. doi: 10.1016/j.asej.2021.101680.
  • Li, Hong, Duo Yang, Wenzhe Su, Jinhu Lu, and Xinghuo Yu. 2019. “An Overall Distribution Particle Swarm Optimization MPPT Algorithm for Photovoltaic System Under Partial Shading.” IEEE Transactions on Industrial Electronics 66(1):265–75. doi: 10.1109/TIE.2018.2829668.
  • Majeed Shaikh, Abdul, Mohammad Fawad Shaikh, Shoaib Ahmed Shaikh, Moez Krichen, Rehan Ali Rahimoon, and Abdul Qadir. 2023. “Comparative Analysis of Different MPPT Techniques Using Boost Converter for Photovoltaic Systems under Dynamic Shading Conditions.” Sustainable Energy Technologies and Assessments 57(August 2022):103259. doi: 10.1016/j.seta.2023.103259.
  • Mao, Mingxuan, Lichuang Cui, Qianjin Zhang, Ke Guo, Lin Zhou, and Han Huang. 2020. “Classification and Summarization of Solar Photovoltaic MPPT Techniques: A Review Based on Traditional and Intelligent Control Strategies.” Energy Reports 6:1312–27. doi: 10.1016/j.egyr.2020.05.013.
  • Mittal, Poornima, Tarush Goel, and Pratyush Gupta. 2020. “Evolution of MPPT Algorithms in Solar Arrays.” Materials Today: Proceedings 37(Part 2):3154–58. doi: 10.1016/j.matpr.2020.09.045.
  • Miyatake, Masafumi, Fuhito Toriumi, Tsugio Endo, and Nobuhiko Fujii. 2007. “A Novel Maximum Power Point Tracker Controlling Several Converters Connected to Photovoltaic Arrays with Particle Swarm Optimization Technique.” Pp. 1–10 in 2007 European Conference on Power Electronics and Applications. IEEE.
  • Mountassir, Salaheddine, Saad Sarih, and Abdelouahed Tajer. 2022. “A FUZZY LOGIC MPPT BASED CONTROL FOR A PHOTOVOLTAIC SYSTEM.” Journal of Theoretical and Applied Information Technology 100(11):3730–38.
  • Obukhov, Sergey, Ahmed Ibrahim, Ahmed A. Zaki Diab, Ameena Saad Al-Sumaiti, and Raef Aboelsaud. 2020. “Optimal Performance of Dynamic Particle Swarm Optimization Based Maximum Power Trackers for Stand-Alone PV System Under Partial Shading Conditions.” IEEE Access 8:20770–85. doi: 10.1109/ACCESS.2020.2966430.
  • Pragallapati, Nataraj, Tanuj Sen, and Vivek Agarwal. 2017. “Adaptive Velocity PSO for Global Maximum Power Control of a PV Array Under Nonuniform Irradiation Conditions.” IEEE Journal of Photovoltaics 7(2):624–39. doi: 10.1109/JPHOTOV.2016.2629844.
  • Priyadarshi, Neeraj, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen, Frede Blaabjerg, and Mahajan Sagar Bhaskar. 2020. “An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration Under Fluctuating Sun Irradiance.” IEEE Systems Journal 14(1):1218–29. doi: 10.1109/JSYST.2019.2949083.
  • Rahman, Md. Motakabbir, and Md. Shahidul Islam. 2020. “PSO and ANN Based Hybrid MPPT Algorithm for Photovoltaic Array under Partial Shading Condition.” Engineering International 8(1):9–24. doi: 10.18034/ei.v8i1.481.
  • Rastogi, Digant, Manika Jain, and Mini Sreejeth. 2022. “Comparative Study of DC-DC Converters in PV Systems Using Fuzzy Logic MPPT Algorithm.” Pp. 1–7 in 2022 IEEE Delhi Section Conference (DELCON). IEEE.
  • Sai, Boni Satya Varun, Sarang A. Khadtare, and Debashis Chatterjee. 2023. “An Improved Weather Adaptable P&O MPPT Technique under Varying Irradiation Condition.” ISA Transactions (xxxx). doi: 10.1016/j.isatra.2023.05.025.
  • Sarvi, Mohammad, and Ahmad Azadian. 2022. “A Comprehensive Review and Classified Comparison of MPPT Algorithms in PV Systems.” Energy Systems 13(2):281–320. doi: 10.1007/s12667-021-00427-x.
  • Wasim, Muhammad Shahid, Muhammad Amjad, Salman Habib, Muhammad Abbas Abbasi, Abdul Rauf Bhatti, and S. M. Muyeen. 2022. “A Critical Review and Performance Comparisons of Swarm-Based Optimization Algorithms in Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions.” Energy Reports 8:4871–98. doi: 10.1016/j.egyr.2022.03.175.
  • Zhang, Wei, Guopeng Zhou, Hao Ni, and Yunlian Sun. 2019. “A Modified Hybrid Maximum Power Point Tracking Method for Photovoltaic Arrays Under Partially Shading Condition.” IEEE Access 7:160091–100. doi: 10.1109/ACCESS.2019.2950375.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Fotovoltaik Güç Sistemleri, Elektrik Mühendisliği (Diğer)
Bölüm Elektrik Elektronik Mühendisliği
Yazarlar

Elif Baldan 0009-0007-1248-4064

Hüseyin Erişti 0000-0003-1474-9170

Yayımlanma Tarihi 3 Aralık 2023
Gönderilme Tarihi 22 Haziran 2023
Yayımlandığı Sayı Yıl 2023Cilt: 26 Sayı: 4

Kaynak Göster

APA Baldan, E., & Erişti, H. (2023). PARÇALI GÖLGELENME DURUMUNDA YAPAY SİNİR AĞLARI VE PARÇACIK SÜRÜ OPTİMİZASYONU TABANLI BİR MAKSİMUM GÜÇ NOKTASI TAKİBİ ALGORİTMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(4), 895-908. https://doi.org/10.17780/ksujes.1318480
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