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Scientific Reports volume 16, Article number: 7902 (2026)
678
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This paper presents a standalone PV curve tracer designed to extract current-voltage (I-V) and power-voltage (P-V) characteristics, as well as the five parameters of a Multiple-Diode Model (MDM) with identical diodes, effectively reducing it to a single-diode model for parameter extraction, under real sunlight conditions. The system consists of a custom-built synchronous boost converter operating as a programmable electronic load, where the duty cycle of the PWM signal regulates the equivalent resistance. Integrated with voltage and current sensors, along with external solar irradiance and temperature sensors, the system enables the automatic extraction of I-V and P-V curves for PV panels up to 300 W. Experiments were conducted on a 12-cell ET-M53630WW PV panel (VOC = 21.52 V, ISC = 1.8 A, VMP = 17.72 V, IMP = 1.69 A and PMP = 30 W), demonstrating the system’s performance under realistic operating conditions. A nonlinear least squares fitting algorithm based on the Levenberg-Marquardt method processes the extracted curves to determine the five key parameters of the PV panel with high precision. Experimental validation demonstrated that the estimated and measured I-V curves are nearly identical, with a worst-case current deviation of only 41.5 mA. The reconstructed P-V curve at standard test conditions (G = 1000 W/m², T = 298.15 K) closely matched the manufacturer’s datasheet, yielding a maximum power of 27 W compared to the rated 30 W. These results highlight the accuracy of the proposed methodology in parameter extraction and its potential application in PV performance monitoring, degradation analysis, and educational use.
The growing global demand for clean and sustainable energy has accelerated the adoption of renewable energy sources, with solar photovoltaic (PV) technology playing a pivotal role in the transition away from fossil fuels. Solar energy offers an abundant and environmentally friendly alternative, making PV systems a key component of modern energy infrastructure1,2,3. The advantages of PV systems, such as their scalability, modularity, and ability to harness sunlight directly, make them one of the most promising solutions for achieving energy sustainability4. However, maximizing the efficiency and reliability of PV panels requires a thorough understanding of their electrical characteristics and how they respond to varying environmental conditions5,6,7.
One of the most widely used models for characterizing PV cells is the Single Diode Model (SDM)8, which provides a simplified yet effective representation of the electrical behavior of a PV cell. This model considers the PV cell as a current source in parallel with a diode, incorporating series and shunt resistances to account for internal losses. The SDM is particularly suitable for analyzing and predicting the behavior of a single PV cell, enabling the extraction of five key parameters (photocurrent, diode ideality factor, series resistance, shunt resistance, and reverse saturation current). These parameters are essential for accurately simulating I-V and P-V characteristics under varying irradiance and temperature conditions9,10,11.
However, PV panels typically consist of multiple cells connected in series, and modeling them using the SDM requires adjusting parameters for each individual cell. To account for this, our study employs a Multiple-Diode Model (MDM) with identical diodes, effectively incorporating the number of series-connected cells while maintaining the same set of parameters for each diode. This approach combines the flexibility of the MDM with the simplicity of the SDM, allowing for accurate extraction of panel-level characteristics without complicating the fitting process.
Other models reported in the literature include the two-diode model, which introduces an additional diode to better capture recombination losses in low-voltage regions, and empirical or semi-empirical models that fit I-V curves based on measured data without relying on physical parameters. While these models can provide increased accuracy under specific conditions, they often require more complex parameter extraction procedures or extensive experimental data, making them less practical for rapid, in-field characterization12,13.
Accurate PV characterization relies not only on modeling but also on the ability to acquire high-quality experimental data. A PV curve tracer is an indispensable tool for this purpose, as it enables the measurement of I-V and P-V curves, revealing the non-linear behavior of PV panels and their sensitivity to external influences such as solar irradiance and ambient temperature14,15,16. These measurements provide valuable insights into operational dynamics, efficiency losses, and degradation mechanisms, all of which are essential for optimizing PV system performance over time. However, commercial PV tracers are often prohibitively expensive, making them inaccessible for many researchers and small-scale solar energy projects. These high costs stem from the complexity of precision electronics, advanced measurement capabilities, and specialized software required to analyze the data effectively.
DC-to-DC power converters play a fundamental role in PV applications, serving as an interface between PV modules and loads to ensure efficient power transfer and voltage regulation17,18,19,20,21. By dynamically adjusting the electrical load seen by the PV panel using Maximum Power Point Tracking (MPPT), these converters help extract maximum power under fluctuating environmental conditions. Additionally, a DC-to-DC converter can act as a variable load when connected to a fixed-value resistor, allowing for precise control over the power drawn from the PV panel. Among various converter topologies, the synchronous boost converter stands out for its efficiency and ability to maintain stable power conversion with minimal losses.
Several hardware approaches have been proposed in the literature to obtain I-V and P-V curves from PV panels. Early methods relied on resistive or capacitive loads to sweep the PV operating point, which are simple to implement but often suffer from limited resolution, poor repeatability, and slow measurement times22,23,24. Commercial electronic loads provide precise and fast curve acquisition, but they are expensive and typically confined to laboratory environments14. Some studies also integrated basic voltage and current sensors to monitor the panel output during the sweep, while a few considered irradiance and temperature measurements to improve measurement accuracy. Despite these advances, most reported systems focus on single curves under standard test conditions and do not provide continuous or automated outdoor measurements, limiting their applicability for real-world PV performance analysis.
Many existing studies25,26,27,28 rely on a single I-V curve measured under standard test conditions (STC), typically at an irradiance of 1000 W/m² and a given temperature, to extract the five parameters of the SDM. However, this approach overlooks the significant impact of varying irradiance and temperature on key parameters such as the photocurrent and the diode’s reverse saturation current. In reality, the photocurrent is directly influenced by irradiance and temperature levels, while the reverse saturation current is highly temperature-dependent. By assuming a photocurrent and a reverse saturation current without considering weather influences, these studies introduce inaccuracies into the extracted SDM parameters, regardless of the extraction algorithm used. For example, the authors of26,28 extracted five parameters of a PV panel using only one I-V curve without incorporating the effects of irradiance and temperature on the photocurrent and reverse saturation current. They obtained different results each time, which is invalid because there can only be one set of five parameters, not multiple sets with varying values. Even if the extracted parameters from these studies perfectly fit the I-V curve they were derived from, they absolutely do not match the I-V curves under different solar irradiance and temperature conditions. Moreover, achieving STC conditions in real-life outdoor environments is nearly impossible without investing thousands of dollars in a laboratory with highly controlled environmental conditions. This limitation underscores the need for a more detailed SDM, leading to the adoption of MDM and a comprehensive approach that considers various environmental conditions for accurate parameter estimation and enhanced PV modeling.
To bridge this gap, this work presents a standalone PV curve tracer that combines a locally designed synchronous boost converter with voltage, current, irradiance, and temperature sensors to enable accurate real-time extraction of I-V and P-V curves under dynamic weather conditions. The system is capable of determining the five parameters of a multiple-diode model with identical diodes, allowing for accurate simulation and prediction of PV panel behavior under any environmental scenario. The proposed approach provides a cost-effective, accessible, and reliable solution for both research and educational purposes.
The originality and contributions of the paper are summarized as follows:
Design of a synchronous boost-converter-based PV curve tracer that operates as a programmable electronic load, enabling rapid and reliable acquisition of I–V and P–V characteristics without the need for commercial electronic loads or laboratory-grade instrumentation.
Development of an automated measurement framework integrating electrical, irradiance, and temperature sensing to ensure synchronized data acquisition under quasi-static environmental conditions.
Implementation of a nonlinear parameter extraction methodology based on the Levenberg-Marquardt algorithm, allowing accurate estimation of the five parameters of a multiple-diode PV model with identical diodes, effectively reduced to a single-diode representation.
Experimental validation under real sunlight conditions, demonstrating high agreement between measured and reconstructed I–V and P–V curves.
Reconstruction of PV characteristics at standard test conditions, enabling comparison with manufacturer datasheet values and highlighting the potential of the proposed system for PV performance assessment and degradation analysis.
This paper is structured into eight sections, each contributing to a comprehensive understanding of the developed PV tracer and its applications. Section 1 outlines the motivation behind this work, emphasizing the need for a cost-effective PV curve tracer using a boost converter. Section 2 presents the theoretical foundation of PV panel behavior through the MDM and its five key parameters. Section 3 details how a synchronous boost converter functions as a programmable load to extract I-V and P-V curves. Section 4 explains the proposed data acquisition process, utilizing solar irradiance and temperature sensors. Section 5 introduces the nonlinear least squares fitting algorithm used to determine the MDM parameters from measured data. Section 6 describes the experimental setup, including hardware and software components. Section 7 presents the extracted PV characteristics, evaluates the accuracy of parameter estimation, and compares reconstructed I-V and P-V curves with datasheet values. Finally, Sect. 8 summarizes the findings, discusses the reliability of the proposed method, and suggests future improvements for PV panel characterization.
A PV panel is a semiconductor-based device that converts solar radiation into electrical energy through the photovoltaic effect. It consists of multiple photovoltaic cells generally connected in series to achieve the desired voltage and current characteristics. Its electrical behavior is represented by the MDM under the condition that all diodes are identical, which simplifies parameter extraction. The equivalent circuit represented in Fig. 1 consists of:
A photocurrent source ((:{I}_{ph})) that depends on solar irradiance.
A set of diodes, equal to the number of series-connected PV cells, representing recombination losses within the semiconductor junction.
A series resistance ((:{R}_{s})) accounting for internal resistive losses.
A shunt resistance ((:{R}_{sh})) representing leakage currents due to defects.
Electrical model of a PV panel.
The output current (:{I}_{PV}) of a PV panel based on the MDM is given by:
Where:
(:{V}_{PV}) represents the voltage of the PV panel.
(:{N}_{s}) the number of PV cell connected in series.
(:q) is the absolute charge of an electron.
(:k) is Boltzmann’s constant.
The other five key parameters are responsible of defining the electrical behavior of the panel:
1) Photocurrent (:left[{I}_{ph}left(G,Tright)right]) represents the light-generated current in the PV cell, which is directly proportional to solar irradiance and temperature:
where:
• (:{I}_{ph,ref}) represents the photocurrent at (STC) and serves as the primary parameter of a PV panel.
• (:{alpha:}_{I}) is the temperature coefficient of short circuit current in (%/K).
• (:T) is the cell temperature in (K).
• (:{T}_{ref}) is the reference temperature (298.15 K).
• (:G) is the incident solar irradiance in (W/m²).
• (:{G}_{ref}) is the reference irradiance (1000 W/m²).
Since (:{I}_{ph}) increases with irradiance, PV panels generate more current on sunny days. However, higher temperatures slightly increase (:{varvec{I}}_{varvec{p}varvec{h}}) while reducing voltage, affecting overall efficiency.
2) Diode reverse saturation current (:left[{I}_{0}left(Tright)right]) represents the reverse leakage current of the diode, which increases exponentially with temperature:
where:
• (:{I}_{0,ref}) is the diode saturation current at (STC) and represents the second key parameter of a PV panel.
• (:{E}_{g,ref}) is the bandgap energy of the semiconductor at reference temperature. (1.12eV)
A higher (:{I}_{0}left(Tright)) leads to increased recombination losses, reducing the open-circuit voltage ((:{varvec{V}}_{varvec{o}varvec{c}})). This explains why PV panels experience voltage drop at high temperatures.
3) Diode ideality factor ((:n)), the third parameter, indicates how closely the diode behaves like an ideal device, influencing the exponential term in the diode equation. Its value is typically greater than 1 and varies based on the cell material and fabrication quality.
4) Series resistance ((:{R}_{s})), the fourth parameter, represents the internal resistance of the PV panel, including:
• Contact resistance of the metal-semiconductor junction.
• Resistance of the semiconductor material.
• Interconnect resistance between PV cells.
A high (:{varvec{R}}_{varvec{s}}) limits the maximum power output. It is most noticeable in the steepness of the I-V curve near (:{varvec{V}}_{varvec{o}varvec{c}}).
5) Shunt resistance ((:{R}_{sh})), the fifth parameter, accounts for leakage currents due to manufacturing defects, microcracks, or impurities in the semiconductor. A (:{R}_{sh}) minimizes leakage, improving efficiency, whereas a low (:{R}_{sh}) leads to higher recombination losses, significantly affecting the short-circuit current (:{varvec{I}}_{varvec{s}varvec{c}}).
Extracting these five key parameters enables the estimation of the PV panel’s generated current under any weather conditions. However, even with these parameters known, directly plotting the current as a function of voltage using Eq. (1) is infeasible due to its non-linear nature. The only viable approach is to employ a numerical method to solve the following equation:
Figure 2 illustrates the typical Current-Voltage (I-V) and Power-Voltage (P-V) characteristics of a PV panel under varying solar irradiance and temperature conditions, estimated using Eq. (4). It is evident from the figures that solar irradiance has a minimal impact on the open-circuit voltage but significantly increases both the short-circuit current and the power output of the PV panel, as confirmed by Eq. (2). Conversely, temperature has a negligible effect on the short-circuit current but moderately reduces the open-circuit voltage and overall power generation.
This highlights the importance of incorporating data from various weather conditions to accurately extract the five key parameters of a PV panel.
Typical Current-Voltage and Power-Voltage characteristics of a PV panel (A) For fixed temperature and variable solar irradiance (B) For fixed solar irradiance and variable temperature.
When a resistor is connected to a PV panel, as illustrated in Fig. 3, the panel operates at a single operating point, as shown in Fig. 4. This point is determined by the intersection of the curves from Eq. 1, the line from Eq. 5, and the prevailing solar irradiance and temperature conditions. To modify the slope of the load line and explore different operating points, the load resistance (:{R}_{Load}), must be varied. A low resistance (close to 0Ω) forces the panel into short-circuit operation, while a high resistance drives it toward open-circuit conditions. This adjustment can be achieved using a mechanical variable resistor, but the process is labor-intensive, time-consuming, and prone to significant errors in parameter extraction.
A more precise and efficient alternative is to use a DC/DC boost converter with a fixed load resistor (:{R}_{Load}), as shown in Fig. 5. The converter effectively acts as an electronic variable resistor, governed by Eq. 6, where the duty cycle (:d) controls the equivalent resistance (:{R}_{DC/DC}). As (:d) varies from 0 to 1, (:{R}_{DC/DC}) transitions from (:{R}_{Load}) to nearly 0Ω. To capture all possible operating points covered in red under specific weather conditions (Fig. 6), (:{R}_{Load}) must be sufficiently high. However, if (:{R}_{Load}) is too large, the output voltage may exceed the safe limits of the MOSFETs and output capacitor, potentially leading to converter failure and leaving some operating points unreachable.
Fortunately, PV panels exhibit near-linear behavior at the two extremes (short-circuit current and open-circuit voltage). This allows for the approximation of missing blue points using tangent extrapolation at these extremes, ensuring a more complete characterization of the system.
PV connected to fixed value resistor (:{R}_{load}).
Operating points of PV panel when connected to fixed value resistor (:{R}_{load}).
Equivalent schematic of DC/DC based variable resistor.
Accessible operating points using a Boost converter.
Figure 7 provides an overview of the main components of the PV tracer, with the detailed specifications of each block summarized in Table 1.
• A PV Panel: The source from which the PV characteristics are extracted.
• A DC/DC Power Converter formed by:
o A synchronous boost converter for voltage regulation.
o A driver circuit to control the MOSFET switching.
o An acquisition and control circuit with an ESP8266 microcontroller (80 MHz, Wi-Fi enabled).
• Sensors for measuring voltage, current, irradiance, and temperature.
• Power Resistor: Serves as a fixed load for the system.
The PV panel under test is connected to the input of the boost converter, as shown in Fig. 7. The microcontroller is connected to a computer using TCP (Transmission Control Protocol), a standard network protocol that ensures reliable, ordered, and error-checked delivery of data between devices. In this setup, the microcontroller acts as a TCP server and the computer functions as a TCP client, communicating with each other at predefined intervals. (Fig. 8).
The TCP client was implemented on a personal computer equipped with an Intel Core i5 processor, 8 GB of RAM, and running a 64-bit Windows 10. The client application was developed using Python, utilizing standard socket libraries for TCP communication and spreadsheet libraries for data logging and storage.
Every sampling period of (:{t}_{S}=:)5 min, the computer commands the microcontroller to gradually adjust the duty cycle from 5% to 95% in 1% steps. For each step, the microcontroller records the PV panel’s voltage and current, along with the irradiance and temperature conditions. This process is executed within a few seconds, ensuring that environmental variations have minimal impact and can be considered constant. The microcontroller then structures the collected data which represents a single I-V curve and transmits it back to the computer, which timestamps and stores it in a spreadsheet. This cycle repeats continuously until the experiment is complete.
A detailed flowchart of the computer and microcontroller algorithms is provided in Fig. 9.
Schematic of the PV curve tracer and parameters extraction.
Timing of PV curve extraction.
PV curves extraction algorithm.
After generating an extensive set of PV characteristic curves using the previous methodology, the acquired operating points each consisting of four key measurements (solar irradiance G, temperature , PV voltage (:{V}_{PV}), and PV current (:{I}_{PV})) are processed using a nonlinear least square fitting algorithm.
This optimization is based on the Levenberg-Marquardt algorithm, which iteratively minimizes the difference between the measured and modeled current. The algorithm relies on:
• A residual function (Eq. 7) that quantifies the deviation between the measured and modeled current values.
• The Jacobian matrix (Eq. 8), which contains the partial derivatives of the residuals with respect to each of the five parameters ((:{I}_{ph,ref}:,:{I}_{0,ref}:,:n:,:{R}_{S}:,:{R}_{sh})) calculated using Eqs. 9,10,11,12,13 forming an N×5 matrix.
• Each iteration updates the five parameters using the update equation (Eq. 14). To ensure physically meaningful results and prevent unrealistic parameter values, lower and upper bounds are imposed on the parameters, restricting them to reasonable intervals.
The overall parameter extraction algorithm, including all iterative steps and constraints, is illustrated in Fig. 10.
To evaluate the accuracy of the estimated current values derived from the five extracted parameters, Absolute Error (AE) (Eq. 15), Mean Absolute Error (MAE) (Eq. 16), and Root Mean Square Error (RMSE) (Eq. 17) are used to compare the estimated values against the measured ones. This evaluation not only assesses the accuracy of the estimated current but also indicates how closely the extracted parameters approximate the true values.
Five parameters extraction algorithm.
To evaluate the effectiveness of the methodologies used in this work, we developed a test bench, as shown in Fig. 11. The setup includes a 30Wc PV panel, whose characteristics at STC are listed in Table 1. A solar irradiance sensor, connected in parallel to the panel, measures the incident irradiance, while a temperature sensor, glued to the back of the panel, monitors its temperature. The PV panel is connected to the input of a power converter illustrated in Fig. 12, which integrates voltage and current sensors for real-time measurements, these sensors were already calibrated using external lab multimeters. At the output, a 160Ω resistive load is connected to safely dissipate the energy generated by the PV.
The entire system is wirelessly connected to a computer via TCP protocol, enabling remote control and data exchange. A 12 V power supply powers the MOSFET driver and is also stepped down to 5 V and 3.3 V to supply other components, including the current sensor, the analog-to-digital converter, and the microcontroller (Table 2).
PV curves extraction bench.
Power Converter.
The experiment was conducted on May 29, 2024, in Oujda, a clear and stable day. The PV panel was placed on the rooftop of the laboratory facing south. The experiment began at 08:21 AM, and every 5 min, the PV converter measured solar irradiance, temperature, voltage, and current for each duty cycle value from 5% to 95% in 1% increments, resulting in a total of 91 operating points under nearly identical weather conditions. The recorded data were then transmitted to the computer for storage. This process continued until 13:36, when solar irradiance reached its peak value, marking the conclusion of the experiment.
However, TCP communication failures occasionally occurred due to network congestion, causing some data curves to be skipped. As a result, only 55 curves were successfully extracted, capturing a total of 5005 operating points under various weather conditions.
Table 3 presents a sample of the extracted data, with one dataset recorded at the beginning and another at the end of the experiment. Additionally, Fig. 13 illustrates the Current-Voltage (I-V) and Power-Voltage (P-V) characteristics for five selected curves from the 55 recorded curves, ensuring clarity in data presentation.
All 55 curves were processed using the algorithm described in Fig. 10, which utilizes solar irradiance (G), temperature (T), PV voltage (VPV), PV current (IPV) and initial five parameters’ values in Table 4 to extract the five parameters listed in Table 5. These parameters were then used to estimate the current for each curve from Fig. 13, represented by circles in Fig. 14. Detailed estimated current values and absolute error calculations are provided in Table 6,
Table A1 and Fig. 15, while Table 7 presents the Mean Absolute Error and Root Mean Square Error between the measured and estimated currents. Additionally, Figs. 16 and 17 illustrate the reconstructed I-V and P-V curves: the first with fixed temperature and variable irradiance, and the second with fixed irradiance and variable temperature.
• The power converter effectively captured all critical operating points around the maximum power point region of the I-V curve for each weather condition. However, data points below 3 V and above 18 V were missing due to the duty cycle limitation (5% to 95%), which was set to protect the MOSFETs from short circuits that could potentially damage the converter. The absence of certain points is also attributed to the low value of the load resistor; however, increasing this resistance would raise the output voltage, potentially exceeding the MOSFET’s and output capacitor rated voltage and causing failure. Despite these constraints, the extracted curves remained highly detailed, even with a 1% duty cycle step.
• Figure 13 clearly illustrate that an increase in solar irradiance from 188 W/m² to 968 W/m² significantly boosted the short-circuit current, leading to a higher power output from the PV panel. Conversely, an increase in temperature from 302 K to 326 K caused a slight drop in voltage, which subsequently reduced the power output. These results align with the reconstructed curves in Figs. 15 and 16, confirming that higher irradiance enhances PV performance, while elevated temperatures reduce efficiency.
• The closest extracted curve to STC conditions (G = 1000 W/m², T = 298.15 K) from Fig. 13 corresponds to the curve recorded at 13:36, near the end of the experiment. At this time, the solar irradiance was 968 W/m², and the temperature reached 326 K. Under these conditions, the PV panel generated a maximum power of 21.97 W at a 76% duty cycle. However, this value is lower than the 30 W specified by the manufacturer in Table 1 for standard test conditions. This discrepancy can be attributed to several factors. First, the solar irradiance was slightly below 1000 W/m². Second, the higher temperature (326 K vs. 298.15 K) caused a voltage drop, reducing power output. The reconstructed curve at STC in Fig. 16 or 17 shows this is exactly the case where the maximum power reached 27 W, still lower than the expected 30 W. However, this can be explained by the fact that PV panel had been in use for several years, suggesting that performance degradation over time may have contributed to the lower power generation.
• Figure 14 provides a direct comparison between measured and estimated I-V curves under identical weather conditions, while Fig. 15 illustrates the absolute error between them, demonstrating remarkable accuracy with a worst-case scenario of 41.5 mA at 968 W/m2 and 326 K. This confirms that the calculated five parameters are sufficiently accurate to reconstruct curves under any weather condition. Table 6 and Table A1 provides a detailed version of Figs. 14 and 15, including absolute error calculations, further validating the accuracy of the estimation.
• Table 7 provides valuable insights, showing that the MAE and RMSE achieve their lowest values around 576 W/m², which represents the midpoint of the solar irradiance measurement range. These error metrics increase as G deviates from 576 W/m², either higher or lower, as clearly illustrated in Figs. 14 and 15. This trend can be attributed to the higher number of extracted curves near the center of the irradiance range, whereas fewer curves are available at the extreme values.
• Finally, Table 8 presents a comparison between the PV panel’s datasheet specifications at STC and the values calculated from the reconstructed I-V and P-V curves at STC. The results show a close match between both datasets, considering that the experiment was conducted under real sunlight rather than a controlled laboratory environment. Additionally, measurements were obtained using a lab-made power converter instead of high-precision laboratory-grade multimeters. Given these conditions, the accuracy of the results demonstrates the impressive performance of the lab-made power converter.
Extracted PV current-voltage and power-voltage characteristics at different weather conditions.
Estimated and measured PV current-voltage and power-voltage characteristics at different weather conditions.
Absolute Error between the estimated and measured currents as a function of voltage.
Reconstructed PV current-voltage and power-voltage characteristics for a fixed temperature of 298.15 K and variable irradiances values using the calculated five parameters.
Reconstructed PV current-voltage and power-voltage characteristics for a fixed solar irradiance of 1000 W/m2 and varying temperature values using the calculated five parameters.
In this study, we developed and tested a standalone, lab-made power converter operating in PV tracer mode to extract I-V and P-V curves from a PV panel under real sunlight conditions. The system incorporated solar irradiance and temperature sensors, eliminating the need for expensive laboratory multimeters. The extracted curves were then processed using a nonlinear least squares fitting algorithm based on the Levenberg-Marquardt method to determine the five parameters of the PV panel using the irradiance and temperature dependent multiple-diode model.
The experimental results demonstrated the effectiveness of the power converter in accurately capturing I-V and P-V characteristics of a 30 W PV panel across various weather conditions. The analysis highlighted the influence of solar irradiance and temperature on the panel’s performance, confirming that the estimated and measured I-V and P-V curves were nearly identical, with a worst-case deviation of only 41.5 mA.
Furthermore, the reconstructed I-V and P-V curves at standard test conditions (G = 1000 W/m², T = 298.15 K) closely matched the manufacturer’s datasheet values, with a maximum power of 27 W compared to the specified 30 W. This discrepancy is likely due to the natural degradation of the PV panel over time. This insight suggests that power converters could be utilized to assess PV panel degradation and estimate their operational lifespan, paving the way for further research in PV aging diagnostics.
Building on this work, future research could extend the PV tracer’s capabilities to a wider range of PV technologies, including bifacial and perovskite solar cells, under real-world conditions. While the current study focused on uniform irradiance, partial shading conditions (PSC) represent a significant practical challenge, as they cause multiple local maxima in the P–V curve and complicate parameter extraction. The proposed hardware is capable of acquiring I-V curves under PSC, but the modeling and extraction methodology would need to be adapted to account for multiple local maxima and non-uniform irradiance effects. Incorporating machine learning algorithms into the parameter extraction process could improve accuracy and enable predictive insights under both uniform and shaded conditions.
Additionally, real-time wireless data transmission and cloud-based storage would facilitate remote monitoring and large-scale field deployment. Advancements in converter design could enhance measurement accuracy while enabling energy harvesting applications. Validating this methodology across diverse PV panels, environmental conditions, and shading scenarios would strengthen its applicability for long-term performance analysis, predictive maintenance, and broader PV diagnostics in real-world systems.
The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. In the event of communication, the corresponding author Badre Bossoufi, E-mail: badre.bossoufi@usmba.ac.ma) will respond to any inquiry or request.
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The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP1/8/46.
This research received no funding.
LGEM Laboratory, Mohammed First University, Oujda, Morocco
Mohammed Rhiat, Ilias Atmane, Mostafa El Ouariachi & Kamal Hirech
Higher School of Education and Training, Mohammed First University, Oujda, Morocco
Mohammed Rhiat, Ilias Atmane & Kamal Hirech
Mechanics, Energetics, Systems and Signals Team, Laboratory of Modeling and Scientific Calculus, National School of Applied Sciences, University Mohammed I, BP 696, Oujda, Morocco
Firyal Latrache
Faculty of Sciences & Technics – LSEET Laboratory, Cadi Ayyad University, Marrakech, Morocco
Mustapha Melhaoui
CESI LINEACT, Campus Saint-Nazaire, Saint-Nazaire, France
Imane Ihsane
EHEI School, Oujda, Morocco
Ilias Atmane & Kamal Hirech
Higher School of Technology of Nador, Mohammed First University, Nador, 62000, Morocco
Mourad Yessef
Electrical Department, College of Engineering, Kafrelsheikh University, Kafrelsheikh, 33511, Egypt
Z. M.S. El-Barbary
Department of Electrical Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia
Z. M.S. El-Barbary & Saad A. Alqahtani
Center for Engineering and Technology Innovations, King Khalid University, Abha, 61421, Saudi Arabia
Z. M.S. El-Barbary & Saad A. Alqahtani
LIMAS Laboratory, Fcaulty of Sciences Dhar Elmahraz, Sidi Mohamed Ben Abdellah University, Fez, 30000, Morocco
Badre Bossoufi
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Mohammed Rhiat, Firyal Latrache : Conceptualization, Methodology, Software, Data curation, Writing- Original draft preparation. Mustapha Melhaoui, Ilias Atmane, Imane Ihsane, Mostafa El Ouariachi, Kamal Hirech: Project Administration, Visualization, Investigation, Funding, Writing- Reviewing and Editing, Z.M.S. El-Barbary, Saad A Alqahtani and Mourad Yessef: Supervision, Software, Validation.
Correspondence to Badre Bossoufi.
The authors declare no competing interests.
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Rhiat, M., Latrache, F., Melhaoui, M. et al. An innovative power converter based technique for on-site photovoltaic I-V characterization under natural irradiance. Sci Rep 16, 7902 (2026). https://doi.org/10.1038/s41598-026-39626-w
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