- Tytuł:
- Fault detection and diagnosis of photovoltaic system based on neural networks approach
- Autorzy:
-
Rahmoune, Ben Mohamed
Iratni, Abdelhamid
Amari, Amel Sabrine
Hafaifa, Ahmed
Colak, Ilhami - Powiązania:
- https://bibliotekanauki.pl/articles/2203647.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
- Tematy:
-
photovoltaic system
fault detection
neural networks
diagnostic system
residue evaluation
system fotowoltaiczny
wykrywanie uszkodzeń
sieci neuronowe
system diagnostyczny - Opis:
- Solar energy has become one of the most important renewable energies in the world. With the increasing installation of power plants in the world, the supervision and diagnosis of photovoltaic systems have become an important challenge with the increased occurrence of various internal and external faults. Indeed, this work proposes a new solar power plant diagnosis based on the artificial neural network approach. The developed model was to improve the performance and reliability of the power plant located in Tamanrasset, Algeria, which is subjected to varying weather conditions in terms of radiation and ambient temperature. By using the real data collected from the studied system, this approach allow to increase electricity production and address any issues that may arise quickly, ensuring uninterrupted power supply for the region. Neural networks have shown interesting results with high accuracy. This fault diagnosis approach allows to determine the time of occurrence of a fault affecting the examined PV system. Also, allow an early detection of failures and degradation of the system, which contributes to improving the productivity of this photovoltaic installation. With a significant reduction in the time needed to repair the damage caused by these faults and improve the reliability and continuity of the electrical energy production service.
- Źródło:
-
Diagnostyka; 2023, 24, 3; art. no. 2023303
1641-6414
2449-5220 - Pojawia się w:
- Diagnostyka
- Dostawca treści:
- Biblioteka Nauki
Artykuł