More Web Proxy on the site http://driver.im/

short-paper

Development of a fault detection algorithm for Photovoltaic Systems

Authors:

Stylianos Voutsinas,

Dimitrios Karolidis,

Ioannis Voyiatzis,

Maria SamarakouAuthors Info & Claims

PCI '21: Proceedings of the 25th Pan-Hellenic Conference on Informatics

Pages 84 - 87

https://doi.org/10.1145/3503823.3503839

Published: 22 February 2022 Publication History

Abstract

The use of an algorithm based on data from I-V curves, a simple and cost-effective method for fault detection and identification in Photovoltaic Systems (PVS), is presented. When determining whether or not to invest in a PVS, life expectancy and reliability are critical considerations. In this paper, the development of an I-V curve-based algorithm for fault detection and identification in PVS is presented. The method calculates the single diode model that describes the Photovoltaic cell in use, for the irradiance and temperature of a certain location. After that, a threshold monitoring approach identifies the presence and the nature of a fault. Measurements were performed to certify the ability of the algorithm to detect both the normal operation at maximum power point and the ability to detect and identify errors introduced during the operation of the experiment. The algorithm can identify open-circuit, short-circuit and mismatch faults. The results are promising, implying that the method could be applied in PVS.

Supplementary Material

Presentation slides (p84-voutsinas-supplement.pptx)

Download
3.73 MB

References

[1]

(IRENA) International Renewable Energy Agency. 2020. Renewable energy statistics 2020.

[2]

Luis Hernández-Callejo, Sara Gallardo-Saavedra, and Víctor Alonso-Gómez. 2019. A review of photovoltaic systems: Design, operation and maintenance. Sol. Energy 188, June (2019), 426–440.

[3]

Ran Fu, David Feldman, and Robert Margolis. 2018. 110. U.S. Solar Photovoltaic System Cost Benchmark: Q1 2018. Nrel Novmnber (2018), 1–47. Retrieved from https://www.nrel.gov/docs/fy19osti/72399.pdf

[4]

Labar Hocine, Kelaiaia Mounia Samira, Mesbah Tarek, Necaibia Salah, and Kelaiaia Samia. 2021. Automatic detection of faults in a photovoltaic power plant based on the observation of degradation indicators. Renew. Energy 164, (2021), 603–617.

[5]

Meriem Memiche, Chaima Bouzian, Ayoub Benzahia, and Ammar Moussi. 2020. Effects of dust, soiling, aging, and weather conditions on photovoltaic system performances in a Saharan environment—Case study in Algeria. Glob. Energy Interconnect. 3, 1 (2020), 60–67.

[6]

Jun Ming Huang, Rong Jong Wai, and Wei Gao. 2019. Newly-designed fault diagnostic method for solar photovoltaic generation system based on IV-Curve measurement. IEEE Access 7, (2019), 70919–70932.

[7]

E. Kaplani. 2012. Detection of degradation effects in field-aged c-Si solar cells through IR thermography and digital image processing. Int. J. Photoenergy 2012, (2012).

[8]

A. Mellit, G. M. Tina, and S. A. Kalogirou. 2018. Fault detection and diagnosis methods for photovoltaic systems: A review. Renew. Sustain. Energy Rev. 91, February (2018), 1–17.

[9]

Stylianos Voutsinas, Dimitrios Karolidis, Ioannis Voyiatzis, and Maria Samarakou. 2020. A survey of fault detection and identification methods for Photovoltaic systems based on I-V curves. In 24th Pan-Hellenic Conference on Informatics (PCI 2020), Association for Computing Machinery, New York, USA, 4 pages.

Digital Library

[10]

Stylianos Voutsinas, Dimitrios Karolidis, Ioannis Voyiatzis, and Maria Samarakou. 2021. Photovoltaic Faults: A comparative overview of detection and identification methods. 2021 10th Int. Conf. Mod. Circuits Syst. Technol. MOCAST 2021 (2021), 8–12.

[11]

Solmetric. 2011. Guide To Interpreting I-V Curve Measurements of PV Arrays. Appl. Note PVA-600-1 (2011), 23. Retrieved from http://resources.solmetric.com/get/GuidetoInterpretingIV-Curves.pdf

[12]

Marcelo Gradella Villalva, Jonas Rafael Gazoli, and Ernesto Ruppert Filho. 2009. Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays. IEEE Trans. POWER Electron. 24, 5 (2009).

[13]

Armando Bellini, Stefano Bifaretti, Vincenzo Iacovone, and Cristina Cornaro. 2009. Simplified model of a photovoltaic module. 2009 Appl. Electron. Int. Conf. AE 2009 October (2009), 47–52.

[14]

Pedro Rodriguez, Dezso Sera, Remus Teodorescu, and Pedro Rodriguez. 2014. PV Panel Model Based on Datasheet Values PV panel model based on datasheet values. March (2014), 2392–2396.

[15]

Tadatoshi Takahashi. 2016. PhotovoltaicModelingPython. Retrieved from https://github.com/tadatoshi/photovoltaic_modeling_python

[16]

Nahla Mohamed Abd Alrahim Shannan, Nor Zaihar Yahaya, and Balbir Singh. 2013. Single-diode model and two-diode model of PV modules: A comparison. Proc. - 2013 IEEE Int. Conf. Control Syst. Comput. Eng. ICCSCE 2013 November 2015 (2013), 210–214.

[17]

P. Papageorgas, D. Piromalis, K. Antonakoglou, G. Vokas, D. Tseles, and K. G. Arvanitis. 2013. Smart solar panels: In-situ monitoring of photovoltaic panels based on wired and wireless sensor networks. Energy Procedia 36, (2013), 535–545.

[18]

P. Papageorgas, D. Piromalis, T. Valavanis, S. Kambasis, T. Iliopoulou, and G. Vokas. 2015. A low-cost and fast PV I-V curve tracer based on an open source platform with M2M communication capabilities for preventive monitoring. Energy Procedia 74, (2015), 423–438.

[19]

Jenny Nelson. 2003. The Physics of Solar Cells Imperial College Press, London, 2003.

[20]

SCH. SCH6P - Multicrystalline Solar Cells.pdf. Retrieved from https://cdn.enfsolar.com/Product/pdf/Cell/51831a51cc9b8.pdf

[21]

Harry D. Kambezidis and Basil E. Psiloglou. 2021. Estimation of the optimum energy received by solar energy flat-plate convertors in Greece using typical meteorological years. Part I: South-oriented tilt angles. Appl. Sci. 11, 4 (2021), 1–27.

[22]

Corinne E Packard, John H Wohlgemuth, and Sarah R Kurtz. 2012. Development of a Visual Inspection Checklist for Evaluation of Fielded PV Module Condition. PV Modul. Reliab. Work. March (2012), 54568.

[23]

Jesus C. Hernández and Pedro G. Vidal. 2009. Guidelines for protection against electric shock in PV generators. IEEE Trans. Energy Convers. 24, 1 (2009), 274–282.

[24]

Jay Johnson, Birger Pahl, Charles Luebke, Tom Pier, Theodore Miller, Jason Strauch, Scott Kuszmaul, and Ward Bower. 2011. Photovoltaic DC arc fault detector testing at Sandia National Laboratories. Conf. Rec. IEEE Photovolt. Spec. Conf. June (2011), 003614–003619.

Cited By

Mandourarakis IGogolou VAgorastou ZVoutsinas SRigogiannis NKoutroulis ESiskos SSamarakou MPapanikolaou NKarystinos G(2023)Development of a smart photovoltaic cells systemEnergy Conversion and Management10.1016/j.enconman.2023.117478293(117478)Online publication date: Oct-2023
https://doi.org/10.1016/j.enconman.2023.117478
Voutsinas SKarolidis DVoyiatzis ISamarakou M(2022)Development of an IoT power management system for photovoltaic power plants2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)10.1109/MOCAST54814.2022.9837652(1-5)Online publication date: 8-Jun-2022
https://doi.org/10.1109/MOCAST54814.2022.9837652
Voutsinas SKarolidis DVoyiatzis ISamarakou M(2022)Development of a multi-output feed-forward neural network for fault detection in Photovoltaic SystemsEnergy Reports10.1016/j.egyr.2022.06.1078(33-42)Online publication date: Nov-2022
https://doi.org/10.1016/j.egyr.2022.06.107

Recommendations

Online fault detection and tolerance for photovoltaic energy harvesting systems
ICCAD '12: Proceedings of the International Conference on Computer-Aided Design

Photovoltaic energy harvesting systems (PV systems) are subject to PV cell faults, which decrease the efficiency of PV systems and even shorten the PV system lifespan. Manual PV cell fault detection and elimination are expensive and nearly impossible ...
DC Arc Fault Detection and Protection in Solar Photovoltaic Power Systems
Graceful Degradation in Algorithm-Based Fault Tolerant Multiprocessor Systems

Algorithm-based fault tolerance (ABFT) is a technique which improves the reliability of a multiprocessor system by providing concurrent error detection and fault location capability to it. It encodes data at the system level and modifies the algorithm ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

PCI '21: Proceedings of the 25th Pan-Hellenic Conference on Informatics

November 2021

499 pages

ISBN:9781450395557

DOI:10.1145/3503823

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 February 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Short-paper
Research
Refereed limited

Conference

PCI 2021

PCI 2021: 25th Pan-Hellenic Conference on Informatics

November 26 - 28, 2021

Volos, Greece

Acceptance Rates

Overall Acceptance Rate 190 of 390 submissions, 49%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
52
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)1

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mandourarakis IGogolou VAgorastou ZVoutsinas SRigogiannis NKoutroulis ESiskos SSamarakou MPapanikolaou NKarystinos G(2023)Development of a smart photovoltaic cells systemEnergy Conversion and Management10.1016/j.enconman.2023.117478293(117478)Online publication date: Oct-2023
https://doi.org/10.1016/j.enconman.2023.117478
Voutsinas SKarolidis DVoyiatzis ISamarakou M(2022)Development of an IoT power management system for photovoltaic power plants2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)10.1109/MOCAST54814.2022.9837652(1-5)Online publication date: 8-Jun-2022
https://doi.org/10.1109/MOCAST54814.2022.9837652
Voutsinas SKarolidis DVoyiatzis ISamarakou M(2022)Development of a multi-output feed-forward neural network for fault detection in Photovoltaic SystemsEnergy Reports10.1016/j.egyr.2022.06.1078(33-42)Online publication date: Nov-2022
https://doi.org/10.1016/j.egyr.2022.06.107

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten