An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array
| Autor: | Nawal Cheggaga, Sabri Boulouma, Adrian Ilinca, Selma Tchoketch Kebir |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
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Computer science diagnosis 020209 energy Geography Planning and Development TJ807-830 02 engineering and technology Management Monitoring Policy and Law Fault (power engineering) TD194-195 Fault detection and isolation Renewable energy sources Robustness (computer science) 0202 electrical engineering electronic engineering information engineering GE1-350 ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS automatic monitoring Artificial neural network Environmental effects of industries and plants Renewable Energy Sustainability and the Environment business.industry Process (computing) Probabilistic logic photovoltaic array Pattern recognition Building and Construction 021001 nanoscience & nanotechnology artificial intelligence neural networks fault detection Environmental sciences classification State (computer science) Artificial intelligence 0210 nano-technology business |
| Zdroj: | Sustainability Volume 13 Issue 11 Sustainability, Vol 13, Iss 6194, p 6194 (2021) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su13116194 |
| Popis: | This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness. |
| Databáze: | OpenAIRE |
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