Enhanced Fault Detection in Solar Photovoltaic Modules Using VMD-LSTM Model
Detection of solar PV faults in an Accurate and versatile technique are essential because the safety and efficiency of solar photovoltaic (PV) modules greatly depend on efficient fault identification. However, because it can be challenging to identify complex operating patterns and detect tiny fault...
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| Format: | Article |
| Language: | English |
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Universitas Riau
2024-10-01
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| Series: | International Journal of Electrical, Energy and Power System Engineering |
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| Online Access: | https://ijeepse.id/journal/index.php/ijeepse/article/view/194 |
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| author | Sikandar Shah Syed Bin Li |
| author_facet | Sikandar Shah Syed Bin Li |
| author_sort | Sikandar Shah Syed |
| collection | DOAJ |
| description | Detection of solar PV faults in an Accurate and versatile technique are essential because the safety and efficiency of solar photovoltaic (PV) modules greatly depend on efficient fault identification. However, because it can be challenging to identify complex operating patterns and detect tiny faults, currently methods frequently have low accuracy. This may make it more difficult to validate the models, which would limit their usefulness in the actual world. This paper presents a new fault detection method that combines Empirical Mode Decomposition (EMD) with the power of Long Short-Term Memory (LSTM) networks. Critical features are efficiently extracted from the data by use of an adaptive decomposition of voltage and current signals into Intrinsic Mode Functions k(IMFs) through the use of EMD. An LSTM network that has been trained to recognize complex patterns and periodic connections then processes this information. Our model which has been validated using a PSCAD simulation model, shows notable improvements in accuracy and durability of more than 92% after undergoing thorough testing on a simulated PV system that allows for several fault types and their severities when compared to existing methods. |
| format | Article |
| id | doaj-art-257ecf3f27234a09981d54f4647e9b0f |
| institution | Kabale University |
| issn | 2654-4644 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Universitas Riau |
| record_format | Article |
| series | International Journal of Electrical, Energy and Power System Engineering |
| spelling | doaj-art-257ecf3f27234a09981d54f4647e9b0f2025-02-04T04:33:38ZengUniversitas RiauInternational Journal of Electrical, Energy and Power System Engineering2654-46442024-10-017313014210.31258/ijeepse.7.3.130-142194Enhanced Fault Detection in Solar Photovoltaic Modules Using VMD-LSTM ModelSikandar Shah Syed0Bin Li1College of Electrical, Energy and Power Engineering, Yangzhou University, ChinaCollege of Electrical, Energy and Power Engineering, Yangzhou University, ChinaDetection of solar PV faults in an Accurate and versatile technique are essential because the safety and efficiency of solar photovoltaic (PV) modules greatly depend on efficient fault identification. However, because it can be challenging to identify complex operating patterns and detect tiny faults, currently methods frequently have low accuracy. This may make it more difficult to validate the models, which would limit their usefulness in the actual world. This paper presents a new fault detection method that combines Empirical Mode Decomposition (EMD) with the power of Long Short-Term Memory (LSTM) networks. Critical features are efficiently extracted from the data by use of an adaptive decomposition of voltage and current signals into Intrinsic Mode Functions k(IMFs) through the use of EMD. An LSTM network that has been trained to recognize complex patterns and periodic connections then processes this information. Our model which has been validated using a PSCAD simulation model, shows notable improvements in accuracy and durability of more than 92% after undergoing thorough testing on a simulated PV system that allows for several fault types and their severities when compared to existing methods.https://ijeepse.id/journal/index.php/ijeepse/article/view/194current signal, emd, imf, lstm, pscad simulation, solar photovoltaic. |
| spellingShingle | Sikandar Shah Syed Bin Li Enhanced Fault Detection in Solar Photovoltaic Modules Using VMD-LSTM Model International Journal of Electrical, Energy and Power System Engineering current signal, emd, imf, lstm, pscad simulation, solar photovoltaic. |
| title | Enhanced Fault Detection in Solar Photovoltaic Modules Using VMD-LSTM Model |
| title_full | Enhanced Fault Detection in Solar Photovoltaic Modules Using VMD-LSTM Model |
| title_fullStr | Enhanced Fault Detection in Solar Photovoltaic Modules Using VMD-LSTM Model |
| title_full_unstemmed | Enhanced Fault Detection in Solar Photovoltaic Modules Using VMD-LSTM Model |
| title_short | Enhanced Fault Detection in Solar Photovoltaic Modules Using VMD-LSTM Model |
| title_sort | enhanced fault detection in solar photovoltaic modules using vmd lstm model |
| topic | current signal, emd, imf, lstm, pscad simulation, solar photovoltaic. |
| url | https://ijeepse.id/journal/index.php/ijeepse/article/view/194 |
| work_keys_str_mv | AT sikandarshahsyed enhancedfaultdetectioninsolarphotovoltaicmodulesusingvmdlstmmodel AT binli enhancedfaultdetectioninsolarphotovoltaicmodulesusingvmdlstmmodel |