Current Photovoltaic Research

Korea Photovoltaic Society (한국태양광발전학회)
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Fault Diagnosis of PV String Using Deep-Learning and I-V Curves

딥러닝과 I-V 곡선을 이용한 태양광 스트링 고장 진단

  • Shin, Woo Gyun (Photovoltaics Research Department, Korea Institue of Energy Research) ;
  • Oh, Hyun Gyu (Photovoltaics Research Department, Korea Institue of Energy Research) ;
  • Bae, Soo Hyun (Photovoltaics Research Department, Korea Institue of Energy Research) ;
  • Ju, Young Chul (Photovoltaics Research Department, Korea Institue of Energy Research) ;
  • Hwang, Hye Mi (Photovoltaics Research Department, Korea Institue of Energy Research) ;
  • Ko, Suk Whan (Photovoltaics Research Department, Korea Institue of Energy Research)
  • 신우균 (태양광연구단, 재생에너지연구소, 한국에너지기술연구원) ;
  • 오현규 (태양광연구단, 재생에너지연구소, 한국에너지기술연구원) ;
  • 배수현 (태양광연구단, 재생에너지연구소, 한국에너지기술연구원) ;
  • 주영철 (태양광연구단, 재생에너지연구소, 한국에너지기술연구원) ;
  • 황혜미 (태양광연구단, 재생에너지연구소, 한국에너지기술연구원) ;
  • 고석환 (태양광연구단, 재생에너지연구소, 한국에너지기술연구원)
  • Received : 2022.08.17
  • Accepted : 2022.09.07
  • Published : 2022.09.30
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Abstract

Renewable energy is receiving attention again as a way to realize carbon neutrality to overcome the climate change crisis. Among renewable energy sources, the installation of Photovoltaic is continuously increasing, and as of 2020, the global cumulative installation amount is about 590 GW and the domestic cumulative installation amount is about 17 GW. Accordingly, O&M technology that can analyze the power generation and fault diagnose about PV plants the is required. In this paper, a study was conducted to diagnose fault using I-V curves of PV strings and deep learning. In order to collect the fault I-V curves for learning in the deep learning, faults were simulated. It is partial shade and voltage mismatch, and I-V curves were measured on a sunny day. A two-step data pre-processing technique was applied to minimize variations depending on PV string capacity, irradiance, and PV module temperature, and this was used for learning and validation of deep learning. From the results of the study, it was confirmed that the PV fault diagnosis using I-V curves and deep learning is possible.

Keywords

Acknowledgement

본 연구는 한국에너지기술평가원 에너지기술개발사업의 지원을 받아 수행되었습니다(과제번호: 20213030010340).

References

  1. 한국에너지공단 신재생에너지정책실, 2020년도 신.재생에너지 보급통계(확정치) 결과.
  2. Di Lorenzo, Gianfranco, G., Araneo, R., Mitolo, M., Niccolai, A., & Grimaccia, F., Review of O&M practices in PV plants: Failures, solutions, remote control, and monitoring tools, IEEE Journal of Photovoltaics, 10(4), pp. 914-926 (2020). https://doi.org/10.1109/JPHOTOV.2020.2994531
  3. Klise, Geoffrey Taylor, & Balfour, John., A Best Practice for Developing Availability Guarantee Language in Photovoltaic (PV) O&M Agreements. United States. https://doi.org/10.2172/1227340
  4. Rezk, M., Aljasmi, N., Salim, R., Ismail, H., & Nikolakakos, I., Autonomous PV Panel Inspection With Geotagging Capabilities Using Drone, ASME International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, Vol. 85611, V07AT07A040 (2021).
  5. Henry, C., Poudel, S., Lee, S. W., & Jeong, H,. Automatic detection system of deteriorated PV modules using drone with thermal camera, Applied Sciences, 10(11), 3802(2020). https://doi.org/10.3390/app10113802
  6. Shin, W. G., Shin, J. Y., Hwang, H. M., Park, C. H., & Ko, S. W., Power Generation Prediction of Building-Integrated Photovoltaic System with Colored Modules Using Machine Learning, Energies, 15(7), 2589 (2022). https://doi.org/10.3390/en15072589
  7. Kurukuru, V. S. B., Haque, A., Khan, M. A., Sahoo, S., Malik, A., & Blaabjerg, F., A review on artificial intelligence applications for grid-connected solar photovoltaic systems, Energies, 14(15), 4690 (2021). https://doi.org/10.3390/en14154690