Ensemble LVQ Model for Photovoltaic Line-to-Line Fault Diagnosis Using K-Means Clustering and AdaGrad

Peyman Ghaedi, Aref Eskandari (), Amir Nedaei, Morteza Habibi, Parviz Parvin and Mohammadreza Aghaei ()
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Peyman Ghaedi: Department of Physics and Energy Engineering, Amirkabir University of Technology (AUT), Tehran 15916-34311, Iran
Aref Eskandari: Department of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran 16846-13114, Iran
Amir Nedaei: Department of Electrical Engineering, Amirkabir University of Technology (AUT), Tehran 15916-34311, Iran
Morteza Habibi: Department of Physics and Energy Engineering, Amirkabir University of Technology (AUT), Tehran 15916-34311, Iran
Parviz Parvin: Department of Physics and Energy Engineering, Amirkabir University of Technology (AUT), Tehran 15916-34311, Iran
Mohammadreza Aghaei: Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway

Energies, 2024, vol. 17, issue 21, 1-23

Abstract: Line-to-line (LL) faults are one of the most frequent short-circuit conditions in photovoltaic (PV) arrays which are conventionally detected and cleared by overcurrent protection devices (OCPDs). However, OCPDs are shown to face challenges when detecting LL faults under critical detection conditions, i.e., low mismatch levels and/or high fault impedance values. This occurs due to insufficient fault current, thus leaving the LL faults undetected and leading to power losses and even catastrophic fire hazards. To compensate for OCPD deficiencies, recent studies have proposed modern artificial intelligence (AI)-based methods. However, various limitations can still be witnessed even in AI-based methods, such as (i) most of the models requiring a massive training dataset, (ii) critical fault detection conditions not being taken into consideration, (iii) models not being accurate enough when dealing with critical conditions, etc. To this end, the present paper proposes a learning vector quantization (LVQ)-based ensemble learning model in which three LVQs are individually trained to detect and classify LL faults in PV arrays. The initial LVQ vectors are determined using the k-means clustering method, and the learning rate is optimized by the adaptive gradient (AdaGrad) optimizer. The training and testing datasets are collected according to the PV array’s current–voltage (I–V) characteristic curve, and several features are extracted based on the Canberra and chi-squared distance techniques. The model utilizes a small training dataset, considers various critical detection conditions for LL faults—such as different mismatch levels and fault impedance values—and the final experimental results show that the model achieves an impressive average accuracy of 99.26%.

Keywords: photovoltaic (PV); fault detection; line-to-Line (LL) faults; learning vector quantization (LVQ); K-means clustering; adaptive gradient (AdaGrad) optimizer; ensemble learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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