Deriving and Validating an Irradiance-Dependent Detection-Limit Floor for a Low-Cost Fixed-Load Photovoltaic Fault Monitor

Abstract

Although low-cost photovoltaic (PV) monitoring systems are able to indicate underperformance, they can fall short of detecting a fault due to sensor noise and model uncertainty. This paper presents an irradiance-dependent closed-form-like detection limit, and ?P_min (G), for reduced fixed load PV fault monitor using power residual thresholding. We derive the limit by cascading the measurement error of module voltage, load current, irradiance and cell temperature through a PV module model using a single PV cell. We validate the noise-propagation model using sixteen days of outdoor measurements on a 10 W crystalline-silicon PV module equipped with an ESP32-based logger. The power-residual scatter in the prediction is in good agreement with the scatter observed under steady irradiance within an order of unity, whereas the residuals are much larger during cloud transition periods. The detection limit rises from an estimated 19 mW under low irradiance to an estimated 100 mW under high irradiance, or from about 14% of operating power to about 1.9% of operating power. The voltage-measurement channel is the main contributor to residual variance, meaning the biggest hardware upgrade to make is an improvement to the voltage signal acquisition. The results give a practical guideline for calculating the minimum power loss that can be detected by the use of a low-cost fixed-load PV monitor. Based on the healthy-module data, this study verifies the sensor-noise floor, and we propose controlled fault-injection experiments as the next step in sensor-noise floor validation.

Introduction

This study proposes a low-cost method for detecting faults in photovoltaic (PV) solar modules using electrical measurements and a physics-based single-diode model. The main objective is to determine the minimum fault-related power loss that can theoretically be detected using inexpensive sensors, without requiring costly equipment such as I–V curve tracers or infrared cameras.

The proposed method analyzes voltage, current, irradiance, temperature, and sensor noise to establish a theoretical detection limit. It identifies three major fault categories: increased series resistance caused by corrosion or damaged connections, decreased shunt resistance caused by electrical leakage, and reduced photocurrent caused by shading. The study demonstrates that the voltage-to-current ratio is not a useful fault indicator when a fixed resistive load is used, because this ratio remains equal to the load resistance regardless of the module’s condition. Instead, the method relies on deviations in the measured operating voltage, current, and power from the healthy-module model.

Experimental validation was conducted over 16 days using a single 10 W PV module, a fixed resistive load, and low-cost sensors under outdoor conditions. No controlled faults were introduced; therefore, the experiment validated the sensor-noise model using healthy-module data rather than testing actual fault classification. The auxiliary irradiance sensor was found unreliable, so operating current was used as the primary indicator related to irradiance. A possible discrepancy between the measured and nominal load resistance also highlighted the need for independent voltage calibration.

References

[1] Ember. Global electricity review 2026. Ember, 2026, https://ember-energy.org/latest-insights/global-electricity-review-2026/. [2] IEA Photovoltaic Power Systems Programme. Snapshot of global PV markets 2026. Report IEA-PVPS T1-50:2026, International Energy Agency, 2026, https://iea-pvps.org/snapshot-reports/snapshot-2026/. [3] D. S. Pillai, N. Rajasekar. A comprehensive review on protection challenges and fault diagnosis in PV systems. Renewable and Sustainable Energy Reviews. Vol. 91, pg. 18-40, 2018, https://doi.org/10.1016/j.rser.2018.03.082. [4] A. Mellit, G. M. Tina, S. A. Kalogirou. Fault detection and diagnosis methods for photovoltaic systems: a review. Renewable and Sustainable Energy Reviews. Vol. 91, pg. 1-17, 2018, https://doi.org/10.1016/j.rser.2018.03.062. [5] S. R. Madeti, S. N. Singh. A comprehensive study on different types of faults and detection techniques for solar photovoltaic system. Solar Energy. Vol. 158, pg. 161-185, 2017, https://doi.org/10.1016/j.solener.2017.08.069. [6] I. Alla, T. Iqbal. Design and implementation of a low cost web server using ESP32 for real-time photovoltaic system monitoring. 2017 IEEE Electrical Power and Energy Conference (EPEC). Pg. 1-5, 2017, https://doi.org/10.1109/EPEC.2017.8286184. [7] Y. Cheddadi, H. Cheddadi, F. Cheddadi, F. Errahimi, N. Es-sbai. Design and implementation of an intelligent low-cost IoT solution for energy monitoring of photovoltaic stations. SN Applied Sciences. Vol. 2, pg. 1165, 2020, https://doi.org/10.1007/s42452-020-2997-4. [8] A. López-Vargas, M. Fuentes, M. Vivar. A low-cost, stand-alone sensory platform for monitoring extreme solar overirradiance events. Sensors. Vol. 18, pg. 2685, 2018, https://doi.org/10.3390/s18082685. [9] Waaree Energies Ltd. Waaree solar module datasheet: WS-10/12V polycrystalline module (Surya series). Manufacturer datasheet, 2020. [10] A. M. Humada, M. Hojabri, S. Mekhilef, H. M. Hamada. Solar cell parameters extraction based on single and double-diode models: a review. Renewable and Sustainable Energy Reviews. Vol. 56, pg. 494-509, 2016, https://doi.org/10.1016/j.rser.2015.11.051. [11] T. J. McMahon, T. S. Basso, S. R. Rummel. Cell shunt resistance and photovoltaic module performance. Conference Record of the Twenty Fifth IEEE Photovoltaic Specialists Conference. Pg. 1291-1294, 1996, https://doi.org/10.1109/PVSC.1996.564369. [12] E. Özkalay, F. Valoti, M. Caccivio, A. Virtuani, G. Friesen, C. Ballif. The effect of partial shading on the reliability of photovoltaic modules in the built-environment. EPJ Photovoltaics. Vol. 15, pg. 7, 2024, https://doi.org/10.1051/epjpv/2024001. [13] B. N. Taylor, C. E. Kuyatt. Guidelines for evaluating and expressing the uncertainty of NIST measurement results. NIST Technical Note 1297, National Institute of Standards and Technology, 1994. [14] J. von Neumann, R. H. Kent, H. R. Bellinson, B. I. Hart. The mean square successive difference. Annals of Mathematical Statistics. Vol. 12, pg. 153-162, 1941, https://doi.org/10.1214/aoms/1177731746. [15] A. Livera, M. Theristis, G. Makrides, G. E. Georghiou. Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems. Renewable Energy. Vol. 133, pg. 126-143, 2019, https://doi.org/10.1016/j.renene.2018.09.101. [16] S. K. Firth, K. J. Lomas, S. J. Rees. A simple model of PV system performance and its use in fault detection. Solar Energy. Vol. 84, pg. 624-635, 2010, https://doi.org/10.1016/j.solener.2009.08.004. [17] A. Chouder, S. Silvestre. Automatic supervision and fault detection of PV systems based on power losses analysis. Energy Conversion and Management. Vol. 51, pg. 1929-1937, 2010, https://doi.org/10.1016/j.enconman.2010.02.025. [18] J. Leloux, L. Narvarte, A. Luna, A. Desportes. Automatic fault detection on BIPV systems without solar irradiation data. 29th European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC), Amsterdam, 2014, https://doi.org/10.48550/arXiv.1410.6946.

Copyright

Copyright © 2026 Rudra Sadhukhan, Dr. K. Gobikannan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.