Fault resistance estimation for line-line fault in photovoltaic arrays using regression-based dense neural network

Abstract: Line-line (LL) faults in photovoltaic (PV) arrays lead to a reduction in the output power and service life of PV systems, and if not detected and eliminated in time, may cause catastrophic fire hazards. The severity of LL faults could have an adverse effect on the performance of protection devices and depends on two main factors: 1) the number of modules involved in the fault, and 2) the resistivity of the fault path. In this study, first, LL faults are classified using a support vector machine (SVM) and then the main process of LL faults resistance (Rf) estimation is carried out to determine the accurate severity of the fault. Conventionally, to measure the resistivity of the fault path, it is inevitable to employ multiple additional current and voltage sensors, which does not seem economically convenient and practical. Therefore, in this study, artificial intelligence (AI) has been utilized to estimate the fault resistance thus eliminating all the additional sensors. To this end, a regression-based dense neural network (R-DNN) is proposed and fine-tuned to predict the Rf in a continuous interval of 0–24 Ω. Also, to provide the proposed model with a more organized thus simpler interpretation of the initial dataset, novel tangential features are constructed using five predefined points on the PV array current-voltage (I–V) characteristic curve. The experimental results show that the model is perfectly capable of diagnosing the LL faults and estimating the fault resistance values under various conditions. Finally, a comparison is carried out between the final R-DNN model and several regression-based machine learning algorithms which shows that the proposed R-DNN outperforms the other algorithms in terms of accuracy and execution time

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch
• Notes: Engineering applications of artificial intelligence. - 133 (2024) , 108067, ISSN: 0952-1976

Classification and Topics

• Classification: Elektrotechnik, Elektronik

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Freiburg
• (who): Universität
• (when): 2025
• Creator: Nedaei, Amir ; Eskandari, Aref ; Milimonfared, Jafar ; Aghaei, Mohammadreza

Further information

• DOI: 10.1016/j.engappai.2024.108067
• URN: urn:nbn:de:bsz:25-freidok-2613871
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:31 AM CEST