IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i7p1931-d527528.html
   My bibliography  Save this article

Grid-Connected PV Systems Controlled by Sliding via Wireless Communication

Author

Listed:
  • Juan M. Cano

    (Department of Electrical and Thermal Engineering, Design and Projects, University of Huelva, 21007 Huelva, Spain)

  • Aranzazu D. Martin

    (Department of Electrical and Thermal Engineering, Design and Projects, University of Huelva, 21007 Huelva, Spain)

  • Reyes S. Herrera

    (Department of Electrical and Thermal Engineering, Design and Projects, University of Huelva, 21007 Huelva, Spain)

  • Jesus R. Vazquez

    (Department of Electrical and Thermal Engineering, Design and Projects, University of Huelva, 21007 Huelva, Spain)

  • Francisco Javier Ruiz-Rodriguez

    (Department of Electrical and Thermal Engineering, Design and Projects, University of Huelva, 21007 Huelva, Spain)

Abstract

Grid-connected photovoltaic (PV) systems are designed to provide energy to the grid. This energy transfer must fulfil some requirements such as system stability, power quality and reliability. Thus, the aim of this work is to design and control a grid-connected PV system via wireless to guarantee the correct operation of the system. It is crucial to monitor and supervise the system to control and/or detect faults in real time and in a remote way. To do that, the DC/DC converter and the DC/AC converter of the grid-connected PV system are controlled wirelessly, reducing costs in cabling installations. The used control methods are the sliding for the DC/DC converter and the Proportional-Integral (PI) for the inverter. The sliding control is robust, ensures system stability under perturbations, and is proven to work well via wireless. The PI control is simple and effective, proving its validity through wireless too. In addition, the effect of the communications is analysed in both controllers. An experimental platform has been built to conduct the experiments to verify the operation of the grid-connected PV system remotely. The results show that the system operates well, achieving the desired values for the maximum power point tracker (MPPT) sliding control and the energy transfer from the inverter to the grid.

Suggested Citation

  • Juan M. Cano & Aranzazu D. Martin & Reyes S. Herrera & Jesus R. Vazquez & Francisco Javier Ruiz-Rodriguez, 2021. "Grid-Connected PV Systems Controlled by Sliding via Wireless Communication," Energies, MDPI, vol. 14(7), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1931-:d:527528
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/7/1931/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/7/1931/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Papadakis, Kostas & Koutroulis, Eftichios & Kalaitzakis, Kostas, 2005. "A server database system for remote monitoring and operational evaluation of renewable energy sources plants," Renewable Energy, Elsevier, vol. 30(11), pages 1649-1669.
    2. Jun-Hyun Shin & Jin-O Kim, 2020. "On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant," Energies, MDPI, vol. 13(17), pages 1-12, September.
    3. Sufyan Samara & Emad Natsheh, 2020. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
    4. Qiang Zhao & Shuai Shao & Lingxing Lu & Xin Liu & Honglu Zhu, 2018. "A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm," Energies, MDPI, vol. 11(1), pages 1-21, January.
    5. Aranzazu D. Martin & Juan M. Cano & Reyes S. Herrera & Jesus R. Vazquez, 2019. "Wireless Sliding MPPT Control of Photovoltaic Systems in Distributed Generation Systems," Energies, MDPI, vol. 12(17), pages 1-16, August.
    6. Lyden, S. & Haque, M.E., 2015. "Maximum Power Point Tracking techniques for photovoltaic systems: A comprehensive review and comparative analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1504-1518.
    7. Chen, Zhicong & Wu, Lijun & Cheng, Shuying & Lin, Peijie & Wu, Yue & Lin, Wencheng, 2017. "Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics," Applied Energy, Elsevier, vol. 204(C), pages 912-931.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sunme Park & Soyeong Park & Myungsun Kim & Euiseok Hwang, 2020. "Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems," Energies, MDPI, vol. 13(3), pages 1-16, February.
    2. Elias Roumpakias & Tassos Stamatelos, 2023. "Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    3. Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    4. Ramadoss Janarthanan & R. Uma Maheshwari & Prashant Kumar Shukla & Piyush Kumar Shukla & Seyedali Mirjalili & Manoj Kumar, 2021. "Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems," Energies, MDPI, vol. 14(20), pages 1-19, October.
    5. Imran Hussain & Ihsan Ullah Khalil & Aqsa Islam & Mati Ullah Ahsan & Taosif Iqbal & Md. Shahariar Chowdhury & Kuaanan Techato & Nasim Ullah, 2022. "Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line," Energies, MDPI, vol. 15(14), pages 1-14, July.
    6. Hao Wu & Lin Zhou & Yihao Wan & Qiang Liu & Siyu Zhou, 2019. "A Mixed Uncertainty Power Flow Algorithm-Based Centralized Photovoltaic (PV) Cluster," Energies, MDPI, vol. 12(20), pages 1-16, October.
    7. Arévalo, Paul & Benavides, Dario & Tostado-Véliz, Marcos & Aguado, José A. & Jurado, Francisco, 2023. "Smart monitoring method for photovoltaic systems and failure control based on power smoothing techniques," Renewable Energy, Elsevier, vol. 205(C), pages 366-383.
    8. Diego R. Espinoza Trejo & Ernesto Bárcenas & José E. Hernández Díez & Guillermo Bossio & Gerardo Espinosa Pérez, 2018. "Open- and Short-Circuit Fault Identification for a Boost dc/dc Converter in PV MPPT Systems," Energies, MDPI, vol. 11(3), pages 1-15, March.
    9. Ding, Kun & Chen, Xiang & Weng, Shuai & Liu, Yongjie & Zhang, Jingwei & Li, Yuanliang & Yang, Zenan, 2023. "Health status evaluation of photovoltaic array based on deep belief network and Hausdorff distance," Energy, Elsevier, vol. 262(PB).
    10. Jiang, Joe-Air & Su, Yu-Li & Kuo, Kun-Chang & Wang, Chien-Hao & Liao, Min-Sheng & Wang, Jen-Cheng & Huang, Chen-Kang & Chou, Cheng-Ying & Lee, Chien-Hsing & Shieh, Jyh-Cherng, 2017. "On a hybrid MPPT control scheme to improve energy harvesting performance of traditional two-stage inverters used in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1113-1128.
    11. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
    12. Başoğlu, Mustafa Engin & Çakır, Bekir, 2016. "Comparisons of MPPT performances of isolated and non-isolated DC–DC converters by using a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1100-1113.
    13. Santiago Pindado & Javier Cubas & Elena Roibás-Millán & Francisco Bugallo-Siegel & Félix Sorribes-Palmer, 2018. "Assessment of Explicit Models for Different Photovoltaic Technologies," Energies, MDPI, vol. 11(6), pages 1-22, May.
    14. Kamran Ali & Laiq Khan & Qudrat Khan & Shafaat Ullah & Saghir Ahmad & Sidra Mumtaz & Fazal Wahab Karam & Naghmash, 2019. "Robust Integral Backstepping Based Nonlinear MPPT Control for a PV System," Energies, MDPI, vol. 12(16), pages 1-20, August.
    15. Yousef Alharbi & Ahmed Darwish & Xiandong Ma, 2023. "A Comprehensive Review of Distributed MPPT for Grid-Tied PV Systems at the Sub-Module Level," Energies, MDPI, vol. 16(14), pages 1-23, July.
    16. Jun Su & Zhiyuan Zeng & Chaolong Tang & Zhiquan Liu & Tianyou Li, 2024. "A Photovoltaic Fault Diagnosis Method Integrating Photovoltaic Power Prediction and EWMA Control Chart," Energies, MDPI, vol. 17(17), pages 1-22, August.
    17. Dan Craciunescu & Laurentiu Fara, 2023. "Investigation of the Partial Shading Effect of Photovoltaic Panels and Optimization of Their Performance Based on High-Efficiency FLC Algorithm," Energies, MDPI, vol. 16(3), pages 1-28, January.
    18. Song-Do Ki & Cheol-Woong Choi & Jae-Sub Ko & Dae-Kyong Kim, 2024. "Current-Sensorless Method for Photovoltaic System Using Capacitor Charging Characteristics," Energies, MDPI, vol. 17(19), pages 1-12, October.
    19. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    20. Li, B. & Delpha, C. & Diallo, D. & Migan-Dubois, A., 2021. "Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1931-:d:527528. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.