Fault diagnosis of photovoltaic array based on gated residual network with multi-head self attention mechanism

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dc.contributor.authorBelgacem, Ahmed Mesaı
dc.contributor.authorHadef, Mounir
dc.contributor.authorDjerdır, Abdesslem
dc.date.accessioned2025-01-06T17:23:38Z
dc.date.available2025-01-06T17:23:38Z
dc.date.issued2024
dc.departmentAdana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi
dc.description.abstractEffective fault identification and diagnosis in photovoltaic (PV) arrays is vital for improving the effectiveness, and safety of solar energy systems. While various artificial intelligence methods have successfully established fault detection and diagnosis models, introducing ine?iciencies and potentially overlooking useful features. Moreover, these methods often employ neural networks with limited performance capabilities. In response to these challenges, this paper introduces an innovative intelligent model that integrates a combination of a gated residual neural network (GRN) and a multi-head self-attention mechanism (MHSA). To evaluate the proposed fault diagnosis model, the small-scale PV grid system is implemented, and fault simulation experiments, including arc faults, maximum power tracking failures, line-to-line, open circuit, degradation, and partial shading with normal conditions, are conducted to acquire simulation datasets. Additionally, widely used neural network models, including artificial neural networks, recurrent neural networks, convolutional neural networks, and the proposed model without an attention mechanism, are employed for comparison. Furthermore, common machine learning approaches found in the literature for diagnosing faults of PV arrays, optimized by Bayesian technique are implemented and compared. Simulation results highlight that the proposed approach attains superior performance across key metrics, including accuracy, precision, recall, f1-score, and training e?iciency. Notably, the proposed model achieves an impressive testing accuracy of 99.71%, surpassing alternative methods. This highlights its effectiveness as a robust and e?icient solution for fault diagnosis in PV arrays.
dc.identifier.doi10.55730/1300-0632.4104
dc.identifier.endpage828
dc.identifier.issn1300-0632
dc.identifier.issn1300-0632
dc.identifier.issue6
dc.identifier.startpage806
dc.identifier.trdizinid1283781
dc.identifier.urihttps://doi.org/10.55730/1300-0632.4104
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1283781
dc.identifier.urihttps://hdl.handle.net/20.500.14669/873
dc.identifier.volume32
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectFault detection
dc.subjectattention mechanism
dc.subjectfault diagnosis
dc.subjectPhotovoltaic array
dc.subjectgated residual network
dc.titleFault diagnosis of photovoltaic array based on gated residual network with multi-head self attention mechanism
dc.typeArticle

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