IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i6p2906-d1103619.html
   My bibliography  Save this article

The Study of a Magnetostrictive-Based Shading Detection Method and Device for the Photovoltaic System

Author

Listed:
  • Xiaolei Fu

    (School of Electrical Engineering, Xinjiang University, Urumqi 830017, China)

  • Yizhi Tian

    (School of Electrical Engineering, Xinjiang University, Urumqi 830017, China)

Abstract

When the photovoltaic (PV) system suffers shading problems caused by different degrees and areas, the shaded PV cells will consume electricity and generate heat, the corresponding bypass diode operating at a certain current will conduct, and a special magnetic field will be generated in space. In this study, a magnetostrictive-based shading detection method and device for the PV system are developed from theoretical, simulation, and physical experimental aspects. This study aims to detect the special magnetic field using magnetostrictive material with a certain response pattern under the magnetic field to detect and locate the shading problem of each module in the PV system. Theoretically, the analysis is carried out from the on–off situation of the bypass diodes of PV modules under different shading conditions and the response mechanism of magnetostrictive materials under the action of the magnetic field. During simulation, the finite element magnetic field simulations are performed for the diode and the series magnetic field coil, and the structural parameters of the magnetic field coil are designed based on the simulation results. After establishing the validation idea of the detection method in this study, the experimental platform is built and the experimental steps are designed. Finally, the feasibility of the method proposed in this study is verified, the detection range of the method is calculated, and the minimum spacing of adjacent magnetic field coils is determined by experimental validation. This study provides a novel magnetostrictive-based detection method, as well as a theoretical and experimental basis, for identifying and localizing PV system shading problems, and discusses the feasibility of shading detection at the system level.

Suggested Citation

  • Xiaolei Fu & Yizhi Tian, 2023. "The Study of a Magnetostrictive-Based Shading Detection Method and Device for the Photovoltaic System," Energies, MDPI, vol. 16(6), pages 1-23, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2906-:d:1103619
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/6/2906/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/6/2906/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lee, Chung Geun & Shin, Woo Gyun & Lim, Jong Rok & Kang, Gi Hwan & Ju, Young Chul & Hwang, Hye Mi & Chang, Hyo Sik & Ko, Suk Whan, 2021. "Analysis of electrical and thermal characteristics of PV array under mismatching conditions caused by partial shading and short circuit failure of bypass diodes," Energy, Elsevier, vol. 218(C).
    2. Ko, Suk Whan & Ju, Young Chul & Hwang, Hye Mi & So, Jung Hun & Jung, Young-Seok & Song, Hyung-Jun & Song, Hee-eun & Kim, Soo-Hyun & Kang, Gi Hwan, 2017. "Electric and thermal characteristics of photovoltaic modules under partial shading and with a damaged bypass diode," Energy, Elsevier, vol. 128(C), pages 232-243.
    3. Cavieres, Robinson & Barraza, Rodrigo & Estay, Danilo & Bilbao, José & Valdivia-Lefort, Patricio, 2022. "Automatic soiling and partial shading assessment on PV modules through RGB images analysis," Applied Energy, Elsevier, vol. 306(PA).
    4. Teo, J.C. & Tan, Rodney H.G. & Mok, V.H. & Ramachandaramurthy, Vigna K. & Tan, ChiaKwang, 2020. "Impact of bypass diode forward voltage on maximum power of a photovoltaic system under partial shading conditions," Energy, Elsevier, vol. 191(C).
    5. Bressan, M. & Gutierrez, A. & Garcia Gutierrez, L. & Alonso, C., 2018. "Development of a real-time hot-spot prevention using an emulator of partially shaded PV systems," Renewable Energy, Elsevier, vol. 127(C), pages 334-343.
    6. Ragb, Ola & Bakr, Hanan, 2023. "A new technique for estimation of photovoltaic system and tracking power peaks of PV array under partial shading," Energy, Elsevier, vol. 268(C).
    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. Shen, Yu & He, Zengxiang & Xu, Zhen & Wang, Yiye & Li, Chenxi & Zhang, Jinxia & Zhang, Kanjian & Wei, Haikun, 2022. "Modeling of photovoltaic modules under common shading conditions," Energy, Elsevier, vol. 256(C).
    2. Koo Lee & Sung Bae Cho & Junsin Yi & Hyo Sik Chang, 2022. "Simplified Recovery Process for Resistive Solder Bond (RSB) Hotspots Caused by Poor Soldering of Crystalline Silicon Photovoltaic Modules Using Resin," Energies, MDPI, vol. 15(13), pages 1-19, June.
    3. Rosario Carbone & Cosimo Borrello, 2022. "Experimenting with a Battery-Based Mitigation Technique for Coping with Predictable Partial Shading," Energies, MDPI, vol. 15(11), pages 1-18, June.
    4. Firoozzadeh, Mohammad & Shiravi, Amir Hossein & Lotfi, Marzieh & Aidarova, Saule & Sharipova, Altynay, 2021. "Optimum concentration of carbon black aqueous nanofluid as coolant of photovoltaic modules: A case study," Energy, Elsevier, vol. 225(C).
    5. Cheng Yang & Fuhao Sun & Yujie Zou & Zhipeng Lv & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Haoyang Cui, 2024. "A Survey of Photovoltaic Panel Overlay and Fault Detection Methods," Energies, MDPI, vol. 17(4), pages 1-37, February.
    6. Fan, Siyuan & Wang, Xiao & Wang, Zun & Sun, Bo & Zhang, Zhenhai & Cao, Shengxian & Zhao, Bo & Wang, Yu, 2022. "A novel image enhancement algorithm to determine the dust level on photovoltaic (PV) panels," Renewable Energy, Elsevier, vol. 201(P1), pages 172-180.
    7. Fonseca Alves, Ricardo Henrique & Deus Júnior, Getúlio Antero de & Marra, Enes Gonçalves & Lemos, Rodrigo Pinto, 2021. "Automatic fault classification in photovoltaic modules using Convolutional Neural Networks," Renewable Energy, Elsevier, vol. 179(C), pages 502-516.
    8. Kim, Chungil & Jeong, Myeong Sang & Ko, Jaehwan & Ko, MyeongGeun & Kang, Min Gu & Song, Hyung-Jun, 2021. "Inhomogeneous rear reflector induced hot-spot risk and power loss in building-integrated bifacial c-Si photovoltaic modules," Renewable Energy, Elsevier, vol. 163(C), pages 825-835.
    9. Jeisson Vélez-Sánchez & Juan David Bastidas-Rodríguez & Carlos Andrés Ramos-Paja & Daniel González Montoya & Luz Adriana Trejos-Grisales, 2019. "A Non-Invasive Procedure for Estimating the Exponential Model Parameters of Bypass Diodes in Photovoltaic Modules," Energies, MDPI, vol. 12(2), pages 1-20, January.
    10. Woo Gyun Shin & Suk Whan Ko & Hyung Jun Song & Young Chul Ju & Hye Mi Hwang & Gi Hwan Kang, 2018. "Origin of Bypass Diode Fault in c-Si Photovoltaic Modules: Leakage Current under High Surrounding Temperature," Energies, MDPI, vol. 11(9), pages 1-11, September.
    11. Romênia G. Vieira & Fábio M. U. de Araújo & Mahmoud Dhimish & Maria I. S. Guerra, 2020. "A Comprehensive Review on Bypass Diode Application on Photovoltaic Modules," Energies, MDPI, vol. 13(10), pages 1-21, May.
    12. Luis Miguel Pérez Archila & Juan David Bastidas-Rodríguez & Rodrigo Correa & Luz Adriana Trejos Grisales & Daniel Gonzalez-Montoya, 2020. "A Solution of Implicit Model of Series-Parallel Photovoltaic Arrays by Using Deterministic and Metaheuristic Global Optimization Algorithms," Energies, MDPI, vol. 13(4), pages 1-22, February.
    13. Wang, Haoxuan & Chen, Huaian & Wang, Ben & Jin, Yi & Li, Guiqiang & Kan, Yan, 2022. "High-efficiency low-power microdefect detection in photovoltaic cells via a field programmable gate array-accelerated dual-flow network," Applied Energy, Elsevier, vol. 318(C).
    14. Liu, Yang & Sun, Kangwen & Xu, Ziyuan & Lv, Mingyun, 2022. "Energy efficiency assessment of photovoltaic array on the stratospheric airship under partial shading conditions," Applied Energy, Elsevier, vol. 325(C).
    15. Osmani, Khaled & Haddad, Ahmad & Lemenand, Thierry & Castanier, Bruno & Ramadan, Mohamad, 2021. "An investigation on maximum power extraction algorithms from PV systems with corresponding DC-DC converters," Energy, Elsevier, vol. 224(C).
    16. Yadav, Vinod Kumar & Yadav, Abhishek & Yadav, Ranjana & Mittal, Aaradhya & Wazir, Nadeem Hussain & Gupta, Shubham & Pachauri, Rupendra Kumar & Ghosh, Santosh, 2022. "A novel reconfiguration technique for improvement of PV reliability," Renewable Energy, Elsevier, vol. 182(C), pages 508-520.
    17. Teo, J.C. & Tan, Rodney H.G. & Mok, V.H. & Ramachandaramurthy, Vigna K. & Tan, ChiaKwang, 2020. "Impact of bypass diode forward voltage on maximum power of a photovoltaic system under partial shading conditions," Energy, Elsevier, vol. 191(C).
    18. Lan, Yang & Changshi, Liu, 2023. "Conductance is responsible for the power conversion efficiency of solar cell," Energy, Elsevier, vol. 278(PB).
    19. Yadav, Anurag Singh & Mukherjee, V., 2021. "Conventional and advanced PV array configurations to extract maximum power under partial shading conditions: A review," Renewable Energy, Elsevier, vol. 178(C), pages 977-1005.
    20. Tuyen Nguyen-Duc & Thinh Le-Viet & Duong Nguyen-Dang & Tung Dao-Quang & Minh Bui-Quang, 2022. "Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network," Energies, MDPI, vol. 15(17), pages 1-21, August.

    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:16:y:2023:i:6:p:2906-:d:1103619. 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.