Fault Diagnosis of PV String Using Deep-Learning and I-V Curves

Woo Gyun Shin; Hyun Gyu Oh; Soo Hyun Bae; Young Chul Ju; Hye Mi Hwang; Suk Whan Ko

doi:10.21218/CPR.2022.10.3.077

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2022 Vol.10, Issue 3 Preview Page Next Page

Fault Diagnosis of PV String Using Deep-Learning and I-V Curves 딥러닝과 I-V 곡선을 이용한 태양광 스트링 고장 진단

30 September 2022. pp. 77-83

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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

Deep learning

Fault I-V curves

Data preprocessing

Partial shading

Voltage mismatch

References

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Information

Publisher :Korea Photovoltaic Society
Publisher(Ko) :한국태양광발전학회
Journal Title :Current Photovoltaic Research
Volume : 10
No :3
Pages :77-83
Received Date : 2022-08-17
Revised Date : 2022-09-06
Accepted Date : 2022-09-07
DOI :https://doi.org/10.21218/CPR.2022.10.3.077

Current Photovoltaic Research ISSN:2288-3274(Print) 2508-125X(Online)

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