Detection and Classification of Faults in a Photovoltaic System—A New Hybrid Algorithm

Muhammad Abdullah , Muhammad Qasim Shah , Kashif Habib , Fajar Kabeer , Muhammad Basit Shakir , Rida Zainab , Abdul Kader Sekh

Clean Energy Sustain. ›› 2025, Vol. 3 ›› Issue (4) : 10014

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Clean Energy Sustain. ›› 2025, Vol. 3 ›› Issue (4) :10014 DOI: 10.70322/ces.2025.10014
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Detection and Classification of Faults in a Photovoltaic System—A New Hybrid Algorithm
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Abstract

Four main types of faults can occur at the DC side of any Photovoltaic System (PVS). These faults are quite dangerous and can cause permanent damage to the photovoltaic modules if not addressed promptly. The faults include open circuit, short circuit, degradation, and partial shading. Short circuit faults are classified into line-to-line (L-L) and line-to-ground (L-G). Detecting these faults requires specialized algorithms. This paper tackles this complex issue through (1) fault-finding equations and the placement of current sensors, and (2) a new hybrid algorithm based on data from the fault-finding equations and current sensors. Numerous simulations using PSIM 2021 were conducted to verify this proposed solution. The hybrid algorithm presented here is original compared to previous studies. It is easy to understand, responds quickly, and can be implemented in systems with photovoltaic arrays of any size.

Keywords

Photovoltaic system faults / PV system fault detection / Open circuit faults / Fault detection and classification

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Muhammad Abdullah, Muhammad Qasim Shah, Kashif Habib, Fajar Kabeer, Muhammad Basit Shakir, Rida Zainab, Abdul Kader Sekh. Detection and Classification of Faults in a Photovoltaic System—A New Hybrid Algorithm. Clean Energy Sustain., 2025, 3(4): 10014 DOI:10.70322/ces.2025.10014

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Acknowledgments

The authors acknowledge technical support from the Punjab University in this project.

Author Contributions

Conceptualization, M.A.; Methodology, M.A.; Software, M.A.; Validation, M.A., K.H.; Formal Analysis, M.A.; Investigation, M.A.; Data Curation, M.A.; Writing—Original Draft Preparation, M.A., F.K., A.K.S.; Writing—Review & Editing, M.Q.S., R.Z.; Visualization, M.A., M.Q.S., M.B.S.; Supervision, K.H.; Project Administration, K.H.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data relevant to this study is available upon request; please direct all inquiries to the corresponding author.

Funding

This research received no external funding.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1]

Yi Z, Etemadi AH. Line-to-Line Fault Detection for Photovoltaic Arrays Based on Multiresolution Signal Decomposition and Two-Stage Support. IEEE Trans. Ind. Electron. 2017, 64, 8546-8556.

[2]

Hong YY, Pula RA. Methods of photovoltaic fault detection and classification: A review. Energy Rep. 2022, 8, 5898-5929. doi:10.1016/j.egyr.2022.04.043.

[3]

Triki-Lahiani A, Abdelghani AB, Slama-Belkhodja I. Fault detection and monitoring systems for photovoltaic installations: A review. Renew. Sustain. Energy Rev. 2018, 82, 2680-2692.

[4]

Dhimish M, Holmes V. Fault detection algorithm for grid-connected photovoltaic plants. Sol. Energy 2016, 137, 236-245.

[5]

Vergura S. Hypothesis Tests-Based Analysis for Anomaly Detection in Photovoltaic Systems in the Absence of Environmental Parameters. Energies 2018, 11, 485.

[6]

Chen L, Li S, Wang X. Quickest fault detection in photovoltaic systems. IEEE Trans. Smart Grid 2018, 9, 1835-1847.

[7]

Hariharan R, Chakkarapani M, Ilango GS, Nagamani C. A Method to Detect Photovoltaic Array Faults and Partial Shading in PVSystems. IEEE J. Photovolt. 2016, 6, 1278-1285.

[8]

Hu Y, Zhang J, Cao W, Wu J, Tian GY, Finney SJ, et al. Online Two-Section PV Array Fault Diagnosis with Optimized Voltage Sensor Locations. IEEE Trans. Ind. Electron. 2015, 62, 7237-7246.

[9]

Kumar BP, Ilango GS, Reddy MJ, Chilakapati N. Online Fault Detection and Diagnosis in Photovoltaic Systems Using Wavelet Packets. IEEE J. Photovolt. 2018, 8, 257-265.

[10]

Yahyaoui I, Segatto ME. A practical technique for on-line monitoring of a photovoltaic plant connected to a single-phase grid. Energy Convers. Manag. 2017, 132, 198-206.

[11]

Zhao Y, Ball R, Mosesian J, de Palma JF, Lehman B. Graph-based Semi-supervised Learning for Fault Detection and Classification in Solar Photovoltaic Arrays. IEEE Trans. Power Electron. 2015, 30, 2848-2858.

[12]

Zhao Y, Lehman B, Ball R, Mosesian J, de Palma JF. Outlier detection rules for fault detection in solar photovoltaic arrays. In Proceedings of the 2013 Twenty-Eighth Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Long Beach, CA, USA, 17-21 March 2013; pp. 2913-2920.

[13]

Zhao Y, Balboni F, Arnaud T, Mosesian J, Ball R, Lehman B. Fault experiments in a commercial-scale pv laboratory and fault detection using local outlier factor. In Proceedings of the 2014 IEEE 40th Photovoltaic Specialist Conference (PVSC), Denver, CO, USA, 8-13 June 2014; pp. 3398-3403.

[14]

Yi Z, Etemadi AH. Fault detection for photovoltaic systems based on multi-resolution signal decomposition and fuzzy inference systems. IEEE Trans. Smart Grid 2016, 8, 1274-1283.

[15]

Roy S, Alam MK, Khan F, Johnson J, Flicker J. An irradiance-independent, robust ground-fault detection scheme for pv arrays based on spread spectrum time-domain reflectometry (sstdr). IEEE Trans. Power Electron. 2017, 33, 7046-7057.

[16]

Karmacharya IM, Gokaraju R. Fault location in ungrounded photovoltaic system using wavelets and ann. IEEE Trans. Power Deliv. 2017, 33, 549-559.

[17]

Harrou F, Taghezouit B, Sun Y. Improved k nn-based monitoring schemes for detecting faults in pv systems. IEEE J. Photovolt. 2019, 9, 811-821.

[18]

Pillai DS, Rajasekar N. A comprehensive review on protection challenges and fault diagnosis in PV systems. Renew. Sustain. Energy Rev. 2018, 91, 18-40.

[19]

Hu Y, Cao W, Wu J, Ji B, Holliday D. Thermography-Based Virtual MPPT Scheme for Improving PV Energy Efficiency Under Partial Shading Conditions. IEEE Trans. Power Electron. 2014, 29, 5667-5672.

[20]

Al-Sheikh H, Moubayed N. Fault detection and diagnosis of renewable energy systems: An overview. In Proceedings of the 2012 International Conference on Renewable Energies for Developing Countries (REDEC), Beirut, Lebanon, 28-29 November 2012; pp. 1-7.

[21]

Pillai DS, Rajasekar N. An mppt-based sensorless line- line and line-ground fault detection technique for pv systems. IEEE Trans. Power Electron. 2018, 34, 8646-8659.

[22]

Wang W, Liu AC, Chung HS, Lau RW, Zhang J, Lo AW. Fault diagnosis of photovoltaic panels using dynamic current-voltage characteristics. IEEE Trans. Power Electron. 2015, 31, 1588-1599.

[23]

Murtaza AF, Bilal M, Ahmad R, Sher HA. A circuit analysis based fault finding algorithm for photovoltaic array under ll/lg faults. IEEE J. Emerg. Sel. Top. Power Electron. 2019, 8, 3067-3076.

[24]

Saleh KA, Hooshyar A, El-Saadany EF, Zeineldin HH. Voltage-based protection scheme for faults within utility-scale photovoltaic arrays. IEEE Trans. Smart Grid 2017, 9, 4367-4382.

[25]

Silvestre S, da Silva MA, Chouder A, Guasch D, Karatepe E. New procedure for fault detection in grid connected PV systems based on the evaluation of current and voltage indicators. Energy Convers. Manag. 2014, 86, 241-249.

[26]

Pei T, Hao X. A fault detection method for photovoltaic systems based on voltage and current observation and evaluation. Energies 2019, 12, 1712.

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