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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Solar PV Module Fault Classification using Artificial Intelligence and Machine Learning Techniques |
| Authors Name: | Jitamitra Mohanty , Itun Sarangi , Jagadish Chandra Pati |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_185794
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| Published Paper Id: | IJNRD2301123 |
| Published In: | Volume 8 Issue 1, January-2023 |
| DOI: | |
| Abstract: | Fault analysis in solar photovoltaic (PV) arrays is essential to increase reliability, and improve efficiency and safety in PV systems. Conventional fault protection methods are usually employed to overcome the challenge however conventional protection is only effective in isolating faulty circuits in time of large current flow and remains inactive in case of low fault currents and may cause problems in long run. The model of the different faults emulates the different PV fault conditions which are essential for a healthy PV power system analysis. The model is a solution to classify the potential faults during fault conditions to cut down on the time and cost invested in fault analysis through human analysis. The model is achieved through the use of Artificial Intelligence and Machine learning techniques. The model performance is matched along with specified vectors to check the accuracy using the confusion matrix to ensure good performance in the design. The simulated results determined that the fault diagnosis scheme can correctly classify faults with high efficiency making the power plant troubleshooting process easier. The entire plant characteristics are got from the fed data and the model is trained to capture the entire system behaviour for future instance classification |
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| Cite Article: | "Solar PV Module Fault Classification using Artificial Intelligence and Machine Learning Techniques", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 1, page no.b170-b187, January-2023, Available :http://www.ijnrd.org/papers/IJNRD2301123.pdf |
| Downloads: | 000118750 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2301123 Registration ID: 185794 Published In: Volume 8 Issue 1, January-2023 DOI (Digital Object Identifier): Page No: b170-b187 Country: Puri, Odisha, India Research Area: Electrical Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2301123 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2301123 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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