Computational Complexity Analysis of Arc Fault Detection Algorithms for Photovoltaic Systems

Photovoltaic (PV) systems are increasingly prevalent, but they introduce the risk of arc faults, which can lead to dangerous fires. Arc Fault Detection (AFD) algorithms are crucial for mitigating this danger. Although there are several AFD algorithms, their computational complexity can significantly...

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Bibliographic Details
Published in:Conference record of the IEEE Photovoltaic Specialists Conference pp. 0866 - 0868
Main Authors: Fernandes, Arthur F. S., Da Silva, Joao A. F. G., Barros, Isaac M. S., de Oliveira, Luiz F. P., Barros, Tarcio A. S., Fantinato, Denis G.
Format: Conference Proceeding
Language:English
Published: IEEE 08.06.2025
Subjects:
Accuracy
Computational complexity
Embedded systems
Fast Fourier transforms
Fault detection
Hardware
Photovoltaic systems
Principal component analysis
Real-time systems
Testing
ISSN:2995-1755
Online Access:Get full text
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Summary:Photovoltaic (PV) systems are increasingly prevalent, but they introduce the risk of arc faults, which can lead to dangerous fires. Arc Fault Detection (AFD) algorithms are crucial for mitigating this danger. Although there are several AFD algorithms, their computational complexity can significantly impact real-time performance, especially on resource-limited embedded systems. In that sense, this work analyzes the computational complexity of three representative AFD algorithms based on: Fast Fourier Transform (FFT), Principal Component Analysis (PCA), and Variational Mode Decomposition (VMD). Big-O notation is a common tool for complexity analysis, but it often overlooks constant factors that become significant for small input sizes typical in AFD. We demonstrate these limitations by comparing Big-O estimates with the actual execution times of the three algorithms. The results highlight substantial discrepancies, particularly for VMD, which has the highest complexity. To address this, we propose a refined complexity analysis that incorporates low order terms. This approach provides more accurate execution time estimates, crucial for selecting appropriate AFD algorithms for embedded hardware with limited processing power.
ISSN:2995-1755
DOI:10.1109/PVSC59419.2025.11133275