Fuzzy-Based Fault Detection and Classification in Grid-Connected Floating PV System

Gangolu, Suryanarayana; Sarangi, Saumendra

doi:10.1007/s40313-022-00969-0

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Given the diversity of methods to conduct a diagnostic approach and prevent the earliest degradation of this type of photovoltaic system, such as the work of Noamane Ncir and Nabil El Akchioui in [27], where they proposed an intelligent improvement to determine the maximum power point of a photovoltaic panel using the concept of artificial neural networks. As well as the work of Suryanarayana Gangolu and Saumendra Sarangi in [31], where they developed a fault detection and classification strategy in a photovoltaic system based on fuzzy logic. In addition, Lipsa Priyadarshini et al in [23] developed an intelligent classifier based on Deep Learning LSTM-Based Minimum Variance RVFLN of faults in a gridconnected system.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis of photovoltaic system based on neural networks approach

Rahmoune¹,

Iratni²,

Amari³

et al. 2023

Diagnostyka

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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.

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Section: Introductionmentioning

confidence: 99%