Photovoltaic Module Fault Detection Based on a Convolutional Neural Network Article Swipe
Shiue‐Der Lu
,
Meng-Hui Wang
,
Shao-En Wei
,
Hwa‐Dong Liu
,
Chia‐Chun Wu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/pr9091635
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/pr9091635
With the rapid development of solar energy, the photovoltaic (PV) module fault detection plays an important role in knowing how to enhance the reliability of the solar photovoltaic system and knowing the fault type when a system problem occurs. Therefore, this paper proposed the hybrid algorithm of chaos synchronization detection method (CSDM) with convolutional neural network (CNN) for studying PV module fault detection. Four common PV module states were discussed, including the normal PV module, module breakage, module contact defectiveness and module bypass diode failure. First of all, the defects in 16 pieces of 20W monocrystalline silicon PV modules were preprocessed, and there were four pieces of each fault state. When the signal generator delivered high frequency voltage to the PV module, the original signal was measured and captured by the NI PXI-5105 high-speed data acquisition system (DAS) and was calculated by CSDM, to establish the chaos dynamic error map as the image feature of fault diagnosis. Finally, the CNN was employed for diagnosing the fault state of the PV module. The findings show that after entering 400 random fault data (100 data for each fault) into the proposed method for recognition, the recognition accuracy rate of the proposed method was as high as 99.5%, which is better than the traditional ENN algorithm that had a recognition rate of 86.75%. In addition, the advantage of the proposed algorithm is that the mass original measured data can be reduced by CSDM, the subtle changes in the output signals are captured effectively and displayed in images, and the PV module fault state is accurately recognized by CNN.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/pr9091635
- https://www.mdpi.com/2227-9717/9/9/1635/pdf?version=1631503803
- OA Status
- gold
- Cited By
- 23
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3200578633
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3200578633Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/pr9091635Digital Object Identifier
- Title
-
Photovoltaic Module Fault Detection Based on a Convolutional Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-10Full publication date if available
- Authors
-
Shiue‐Der Lu, Meng-Hui Wang, Shao-En Wei, Hwa‐Dong Liu, Chia‐Chun WuList of authors in order
- Landing page
-
https://doi.org/10.3390/pr9091635Publisher landing page
- PDF URL
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https://www.mdpi.com/2227-9717/9/9/1635/pdf?version=1631503803Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2227-9717/9/9/1635/pdf?version=1631503803Direct OA link when available
- Concepts
-
Photovoltaic system, Convolutional neural network, Fault (geology), Computer science, Fault detection and isolation, Feature (linguistics), Pattern recognition (psychology), Artificial intelligence, Electronic engineering, Engineering, Electrical engineering, Seismology, Philosophy, Linguistics, Actuator, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
23Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 9, 2023: 4, 2022: 5, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2021-09-10 |
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