A Novel Fault Classification Approach for Photovoltaic Systems
Photovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of elect...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/2/308 |
| _version_ | 1798038955881398272 |
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| author | Varaha Satya Bharath Kurukuru Frede Blaabjerg Mohammed Ali Khan Ahteshamul Haque |
| author_facet | Varaha Satya Bharath Kurukuru Frede Blaabjerg Mohammed Ali Khan Ahteshamul Haque |
| author_sort | Varaha Satya Bharath Kurukuru |
| collection | DOAJ |
| description | Photovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of electrical systems. In this work, a fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems. The analysis is carried out considering the feature extraction capabilities of the wavelet transform and classification attributes of radial basis function networks (RBFNs). In order to improve the performance of the proposed classifier, the dynamic fusion of kernels is performed. The performance of the proposed technique is tested on a 1 kW single-phase stand-alone PV system, which depicted a 100% training efficiency under 13 s and 97% testing efficiency under 0.2 s, which is better than the techniques in the literature. The obtained results indicate that the developed method can effectively detect faults with low misclassification. |
| first_indexed | 2024-04-11T21:47:20Z |
| format | Article |
| id | doaj.art-04e6885406a24dfeb3d97cb99b631496 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T21:47:20Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-04e6885406a24dfeb3d97cb99b6314962022-12-22T04:01:23ZengMDPI AGEnergies1996-10732020-01-0113230810.3390/en13020308en13020308A Novel Fault Classification Approach for Photovoltaic SystemsVaraha Satya Bharath Kurukuru0Frede Blaabjerg1Mohammed Ali Khan2Ahteshamul Haque3Advance Power Electronics Research Laboratory, Department of Electrical Engineering, Jamia Millia Islamia (A Central University), New Delhi 110025, IndiaDepartment of Energy Technology, Aalborg University, 9220 Aalborg, DenmarkAdvance Power Electronics Research Laboratory, Department of Electrical Engineering, Jamia Millia Islamia (A Central University), New Delhi 110025, IndiaAdvance Power Electronics Research Laboratory, Department of Electrical Engineering, Jamia Millia Islamia (A Central University), New Delhi 110025, IndiaPhotovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of electrical systems. In this work, a fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems. The analysis is carried out considering the feature extraction capabilities of the wavelet transform and classification attributes of radial basis function networks (RBFNs). In order to improve the performance of the proposed classifier, the dynamic fusion of kernels is performed. The performance of the proposed technique is tested on a 1 kW single-phase stand-alone PV system, which depicted a 100% training efficiency under 13 s and 97% testing efficiency under 0.2 s, which is better than the techniques in the literature. The obtained results indicate that the developed method can effectively detect faults with low misclassification.https://www.mdpi.com/1996-1073/13/2/308photovoltaic systemfault classificationfeature extractionwavelet analysisradial basis function networks (rbfn)kernels |
| spellingShingle | Varaha Satya Bharath Kurukuru Frede Blaabjerg Mohammed Ali Khan Ahteshamul Haque A Novel Fault Classification Approach for Photovoltaic Systems Energies photovoltaic system fault classification feature extraction wavelet analysis radial basis function networks (rbfn) kernels |
| title | A Novel Fault Classification Approach for Photovoltaic Systems |
| title_full | A Novel Fault Classification Approach for Photovoltaic Systems |
| title_fullStr | A Novel Fault Classification Approach for Photovoltaic Systems |
| title_full_unstemmed | A Novel Fault Classification Approach for Photovoltaic Systems |
| title_short | A Novel Fault Classification Approach for Photovoltaic Systems |
| title_sort | novel fault classification approach for photovoltaic systems |
| topic | photovoltaic system fault classification feature extraction wavelet analysis radial basis function networks (rbfn) kernels |
| url | https://www.mdpi.com/1996-1073/13/2/308 |
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