Overview of Black-Box Arc Models and Parameter Identification Techniques for Simulation of PV Systems

Mauricio Taconelli; Luiz F. P. de Oliveira; João A. F. G. da Silva; Denis G. Fantinato; Tarcio A. S. Barros

doi:10.18618/REP.e202455

Authors

Mauricio Taconelli Universidade Estadual de Campinas (UNICAMP) https://orcid.org/0009-0004-0219-6358
Luiz F. P. de Oliveira Universidade Estadual de Campinas (UNICAMP) https://orcid.org/0000-0002-1715-3340
João A. F. G. da Silva Universidade Estadual de Campinas (UNICAMP) https://orcid.org/0000-0002-7488-3614
Denis G. Fantinato Universidade Estadual de Campinas (UNICAMP) https://orcid.org/0000-0002-5009-3431
Tarcio A. S. Barros Universidade Estadual de Campinas (UNICAMP)

DOI:

https://doi.org/10.18618/REP.e202455

Keywords:

DC arcs, metaheuristics, parameters identification, solar energy

Abstract

Solar energy is widely regarded as an environmentally friendly and sustainable source of power. It reduces greenhouse gas emissions and dependency on fossil fuels, contributing to a cleaner environment. It also provides cost savings and enhances energy security. However, technical challenges persist. Poor installation, inadequate maintenance, and aging can degrade photovoltaic (PV) systems, leading to failures or faults. These issues increase the risk of power losses, electrical shocks, and fires. Direct Current (DC) arcs, in particular, pose a significant fire hazard in PV systems due to their unpredictability and high potential for damage. However, accurately defining parameters for real-world DC arc faults is difficult. Developing computational models of electric arcs is essential for simulating, analyzing, and detecting these faults. In that sense, this work provides a comprehensive overview of the prominent black-box arc models documented in the scientific literature, along with various methods for parameter identification, to facilitate the investigation of arc-related incidents within PV systems.

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Author Biographies

Mauricio Taconelli, Universidade Estadual de Campinas (UNICAMP)

PhD student in Electrical Engineering at the State University of Campinas (UNICAMP). Bachelor's degree in Electrical Engineering - Emphasis on Energy Systems and Automation from the University of São Paulo - USP (2017) and Master's degree in Electrical Engineering from the Federal University of São Carlos - UFSCar (2021). Lines of research: power electronics, renewable energy, faults in photovoltaic systems.

Luiz F. P. de Oliveira, Universidade Estadual de Campinas (UNICAMP)

graduated in Electronic Engineering, with Academic Merit, from Federal University of Technology of Paraná (UTFPR, 2017), M.Sc. degree in Electrical and Computer Engineering with specialization in Autonomous Systems from the School of Engineering, Polytechnic Institute of Porto (ISEP-IPP, 2017), and M.Sc. and Ph.D. degree in Electrical Engineering with specialization in Electronics, Microelectronics and Optoelectronics from the University of Campinas (UNICAMP, 2020 and 2023). He is currently a Postdoctoral researcher at the CEPETRO. His research interests are: sensor electronic circuits, microcontroller systems, smart devices, wireless sensor networks, internet of things, smart cities, energy harvesting, robotics, legged robots and hexapod robots. Mr. Oliveira is a Student Member of the Brazilian Automation Society (SBA) and Effective Associate of the Brazilian Society of Microelectronics (SBMicro).

João A. F. G. da Silva, Universidade Estadual de Campinas (UNICAMP)

currently undergrad in electrical engineering by UNICAMP, maintenance electrician by SENAI. Current research topics include power electronics, arc fault in PV systems, PV monitoring and performance.

Denis G. Fantinato, Universidade Estadual de Campinas (UNICAMP)

received the B.S. (2011), M.Sc. (2013) and Ph.D. (2017) degrees in Electrical Engineering from the University of Campinas (UNICAMP), and performed a sandwich Ph.D. (2016) at GIPSA-lab, France. Currently, he is an Assistant Professor at School of Electrical and Computer Engineering (FEEC) at the same university. His main research interests are machine learning, computational intelligence, information-theoretic learning, arc fault in PV systems.

Tarcio A. S. Barros, Universidade Estadual de Campinas (UNICAMP)

holds a Bachelor’s degree in Electrical Engineering from the Federal University of Vale do São Francisco (2010), where he graduated with honors. He earned a Master’s and a Ph.D. in Electrical Engineering at UNICAMP in 2012 and 2015, respectively. Currently, he is an MS5.1 professor at UNICAMP. He is affiliated with IEEE, the Brazilian Society of Automatics (SBA), and the Brazilian Power Electronics Society (SOBRAEP). He is the main coordinator of the LEPO / LESF-MV.

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