A Time–Frequency Composite Recurrence Plots-Based Series Arc Fault Detection Method for Photovoltaic Systems with Different Operating Conditions
Series arc faults (SAFs) pose a significant threat to the safety of photovoltaic (PV) systems. However, the complex operating conditions of PV systems make accurate SAF detection challenging. To tackle this issue, this article proposes a SAF detection method based on time–frequency composite recurre...
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MDPI AG
2025-01-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/9/1/33 |
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| author | Zhendong Yin Hongxia Ouyang Junchi Lu Li Wang Shanshui Yang |
| author_facet | Zhendong Yin Hongxia Ouyang Junchi Lu Li Wang Shanshui Yang |
| author_sort | Zhendong Yin |
| collection | DOAJ |
| description | Series arc faults (SAFs) pose a significant threat to the safety of photovoltaic (PV) systems. However, the complex operating conditions of PV systems make accurate SAF detection challenging. To tackle this issue, this article proposes a SAF detection method based on time–frequency composite recurrence plots (TFCRPs). Initially, variational mode decomposition (VMD) is employed to decompose the current into distinct modes. Subsequently, the proposed TFCRP transforms these modes into two-dimensional matrices, enabling the measurement of composite similarity between different phase states. Lastly, extra tree (ET) is utilized to fuse the fractional recurrence entropy (FRE) and the singular values extracted from the matrices, thereby achieving SAF detection. Experimental results indicate that the proposed method achieves a detection accuracy of 98.75% and can accurately detect SAFs under various operating conditions. Comparisons with different methods further highlight the advancement of the proposed method. Furthermore, the detection time of the proposed method (209 ms) meets the requirements of standard UL1699B. |
| format | Article |
| id | doaj-art-c23fb53b2c744fb7bc319b20748c9ef5 |
| institution | Kabale University |
| issn | 2504-3110 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj-art-c23fb53b2c744fb7bc319b20748c9ef52025-01-24T13:33:26ZengMDPI AGFractal and Fractional2504-31102025-01-01913310.3390/fractalfract9010033A Time–Frequency Composite Recurrence Plots-Based Series Arc Fault Detection Method for Photovoltaic Systems with Different Operating ConditionsZhendong Yin0Hongxia Ouyang1Junchi Lu2Li Wang3Shanshui Yang4College of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, ChinaCollege of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, ChinaCollege of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, ChinaDepartment of Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaDepartment of Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSeries arc faults (SAFs) pose a significant threat to the safety of photovoltaic (PV) systems. However, the complex operating conditions of PV systems make accurate SAF detection challenging. To tackle this issue, this article proposes a SAF detection method based on time–frequency composite recurrence plots (TFCRPs). Initially, variational mode decomposition (VMD) is employed to decompose the current into distinct modes. Subsequently, the proposed TFCRP transforms these modes into two-dimensional matrices, enabling the measurement of composite similarity between different phase states. Lastly, extra tree (ET) is utilized to fuse the fractional recurrence entropy (FRE) and the singular values extracted from the matrices, thereby achieving SAF detection. Experimental results indicate that the proposed method achieves a detection accuracy of 98.75% and can accurately detect SAFs under various operating conditions. Comparisons with different methods further highlight the advancement of the proposed method. Furthermore, the detection time of the proposed method (209 ms) meets the requirements of standard UL1699B.https://www.mdpi.com/2504-3110/9/1/33arc faultvariational mode decompositiontime–frequency composite recurrence plotsfractional recurrence entropysingular value decompositionextra tree |
| spellingShingle | Zhendong Yin Hongxia Ouyang Junchi Lu Li Wang Shanshui Yang A Time–Frequency Composite Recurrence Plots-Based Series Arc Fault Detection Method for Photovoltaic Systems with Different Operating Conditions Fractal and Fractional arc fault variational mode decomposition time–frequency composite recurrence plots fractional recurrence entropy singular value decomposition extra tree |
| title | A Time–Frequency Composite Recurrence Plots-Based Series Arc Fault Detection Method for Photovoltaic Systems with Different Operating Conditions |
| title_full | A Time–Frequency Composite Recurrence Plots-Based Series Arc Fault Detection Method for Photovoltaic Systems with Different Operating Conditions |
| title_fullStr | A Time–Frequency Composite Recurrence Plots-Based Series Arc Fault Detection Method for Photovoltaic Systems with Different Operating Conditions |
| title_full_unstemmed | A Time–Frequency Composite Recurrence Plots-Based Series Arc Fault Detection Method for Photovoltaic Systems with Different Operating Conditions |
| title_short | A Time–Frequency Composite Recurrence Plots-Based Series Arc Fault Detection Method for Photovoltaic Systems with Different Operating Conditions |
| title_sort | time frequency composite recurrence plots based series arc fault detection method for photovoltaic systems with different operating conditions |
| topic | arc fault variational mode decomposition time–frequency composite recurrence plots fractional recurrence entropy singular value decomposition extra tree |
| url | https://www.mdpi.com/2504-3110/9/1/33 |
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