Simulation and Fault Diagnosis Using Current-Voltage Characteristics of Photovoltaic Systems—A Case Study ^†

Abstract

The I-V characteristics of a photovoltaic (PV) system reveal its actual state and performance, and are used for fault detection and diagnosis in PV systems. We reviewed modeling methods for common faults using MATLAB/Simulink R2021a software, including module degradation, open-circuit faults, short-circuit faults, shading faults, and hotspot faults, in this study. A detailed analysis was conducted regarding how these faults impact I-V characteristics. Taking a real PV system as an example, a fault diagnosis case study was carried out. By fitting the measured I-V curves from the PV system and diagnosing potential faults and their severity based on the fitted model parameters, the approach proposed in this study offers a cost-free, simple, and effective detection method. This method can be used by researchers and engineers in the PV field for the advanced fault detection and diagnosis of PV systems.

1. Introduction

As global power demand increases, renewable energy is increasingly used. Renewable energy alleviates the depletion of fossil fuels and mitigates the impacts of climate change, contributing to environmental sustainability [1,2,3,4]. Recently, photovoltaic (PV) technology has been significantly advanced, and the installed capacity of PV systems continues to increase globally [5]. The growth of PV technology has been driven by its pollution-free nature, noiselessness, cost-effectiveness, modularity, ease of installation, reliability, and wide availability [6]. However, PV systems are often deployed in harsh environments, such as on rooftops or in wastelands [7]. Prolonged exposure to these conditions inevitably leads to various faults that negatively impact system performance, reduce power generation, and, in several cases, result in severe hazards such as fires [8,9]. Thus, the implementation of advanced fault detection and diagnosis techniques is critical to improving the reliability, efficiency, and safety of PV systems [10].

References [11,12,13,14,15,16,17] provide a detailed overview of fault types in PV systems and various fault detection and diagnosis methods, which are categorized into visual and thermal methods (VTMs) and electrical-based methods (EBMs). VTMs are mainly used to detect panel damage, discoloration, browning, and surface contamination, while EBMs are used to determine the electrical characteristics of modules or strings in PV systems [16]. As an EBM, the I-V characteristic analysis (IVCA) method is widely used in assessing the performance of PV modules or strings owing to its effectiveness. An I-V curve represents all possible operating currents and voltages of a PV module or string under real-time irradiance and temperature conditions. From this curve, key electrical parameters, such as the open-circuit voltage, short-circuit current, maximum power point, and fill factor, are estimated [9,18]. The I-V curve’s profile changes depending on the fault type and severity. Thus, IVCA is useful in identifying fault types and severity by comparing the output characteristics of modules or strings under both normal and abnormal conditions [19].

In this study, we explored the application of MATLAB/Simulink software to model various types of faults in PV systems, including module degradation as well as open-circuit, short-circuit, shading, and hotspot faults. The I-V characteristics associated with these faults were measured on a PV site and were analyzed. By utilizing curve fitting and analyzing the I-V characteristics, potential faults and their severity in PV systems were diagnosed.

In this article, Section 2 introduces the modeling and validation of the PV model within the simulation framework. Section 3 details the modeling methods for module aging as well as open-circuit, short-circuit, shading, and hotspot faults, along with an analysis of their I-V characteristics. In Section 4, real measurement data from a PV site are presented for fault diagnosis through curve fitting. Finally, Section 5 summarizes the key findings of this paper.

2. Modeling and Simulation

To model various faults in PV systems, the accuracy of the PV site’s simulation model must be established and verified. From the specification of the PV module, the key parameters of the standard test conditions (STC) were identified, as shown in

Table 1. Parameters of PV module (STC).

Parameters	Values
Open-circuit voltage	39.50 V
Short-circuit current	9.95 A
Peak power	305 W
Peak voltage	32.56 V
Peak current	9.37 A

. STC refers to an air mass of 1.5, irradiance of 1000 W/m², and a module temperature of 25 °C [20]. Based on the module parameters, a simulation model of the PV site was developed using MATLAB/Simulink, as illustrated in Figure 1. The PV site operated 22 series-connected modules, each containing 60 cells.

Table 2. Simulation results for PV array (STC).

Parameters	Values
Open-circuit voltage	869 V
Short-circuit current	9.95 A
Peak power	6710 W
Peak voltage	716.11 V
Peak current	9.37 A

presents the simulation results for this PV site. The simulated open-circuit voltage was 869 V for the 22 series-connected modules, and the simulated peak power was 6710 W (305 × 22 = 6710). By comparing the data in Table 1 and Table 2, the accuracy of the simulation model was calculated.

3. Simulation and Analysis of Common Faults

This section outlines the modeling methods for common DC-side faults in photovoltaic (PV) systems using MATLAB/Simulink software, including module degradation as well as open-circuit, short-circuit, shading, and hotspot faults. Additionally, it provides an analysis of the I-V characteristics corresponding to each fault type.

3.1. Module Degradation

Degradation is a phenomenon of material deterioration. PV modules operating in harsh environments undergo aging due to prolonged exposure to elements such as wind and rain [11]. Under ideal conditions, the module’s parallel resistance (R_sh) is high, and its series resistance (R_s) is low, optimizing the conversion efficiency of the PV cells. However, as degradation occurs, R_sh decreases or R_s increases, significantly reducing the module’s output power [21]. In the modeling process, ideal conditions are represented by setting R_sh to 10 Ω and R_s to 0.001 Ω. To simulate module degradation, R_sh is reduced or R_s is increased.

Figure 2 illustrates the effect of changes in R_sh on the I-V characteristics. The figure shows that as R_sh decreases, the current at the maximum power point significantly drops. The slope of the I-V curve gradually decreases as R_sh is reduced from 10 to 0.01 Ω. Figure 3 depicts the effect of changes in R_s on the I-V characteristics, demonstrating that an increase in R_s results in a substantial reduction in the voltage at the maximum power point. The maximum power point decreases as R_s increases.

3.2. Open-Circuit Fault (OCF)

An OCF occurs when one or more strings are disconnected from the PV array, typically due to broken cells or improper connections between components. When a PV string is disconnected, no current flows, resulting in zero power output. Consequently, with an open-circuit fault, both the short-circuit current and maximum power decrease as the number of disconnected strings increases, while the open-circuit voltage remains almost unaffected [14,22]. To simulate an open-circuit fault, a module is disconnected from a string, as shown by the red arrow in Figure 4. When multiple strings in the PV array experience an open-circuit fault, the short-circuit current decreases, but the open-circuit voltage remains nearly constant.

3.3. Short-Circuit Fault (SCF)

An SCF occurs when points with different potentials in the PV array are unintentionally connected, typically due to manufacturing defects or improper connections between components [13]. Since mismatched currents flow through the affected modules, SCFs are extremely dangerous if not detected promptly. When modules in a PV string are short-circuited, only a minimal current flows through the module according to Ohm’s law, causing a reduction in the open-circuit voltage of the PV string. In modeling, short-circuit faults are simulated by connecting both terminals of a module with a wire, as shown by the red arrow in Figure 5. Figure 6 illustrates the I-V characteristics under a short-circuit fault. As the number of short-circuited modules increases, the open-circuit voltage of the PV array decreases, while the short-circuit current remains nearly constant. Therefore, as the open-circuit voltage decreases considerably, short-circuit faults occur in the PV module.

3.4. Shading Fault (SF)

An SF occurs when a part or all of a PV array is shaded, resulting in a reduction in output power. Common causes of shading faults include cloud cover, snow accumulation, and dust pollution, with such faults typically being temporary phenomena [12]. When partial shading faults (PSFs) occur in a PV array, the forward voltage of the bypass diode presents multiple peaks in the I-V curve, indicating a reduction in the maximum power. If the shading persists for an extended period, components operating at high temperatures develop hotspots [21]. In modeling, shading faults can be simulated by reducing the irradiance of certain modules, as shown by the red arrow in Figure 7. Figure 8 illustrates the relationship between the number of shaded modules and I-V characteristics, while Figure 9 explains the relationship between shading irradiance and I-V characteristics.

As the number of shaded modules increases, the voltage at the knee point decreases, while the short-circuit current of the PV array remains nearly constant. Furthermore, as the irradiance of the shaded modules decreases, the current at the knee point also decreases, but the open-circuit voltage of the PV array remains nearly unchanged.

3.5. Hotspot Fault (HSF)

An HSF occurs when a shaded or defective cell becomes a load due to reverse bias voltage, thereby consuming power generated by other cells and causing a local temperature increase [23]. The causes of HSFs include panel aging, snow accumulation, dust pollution, manufacturing defects, and cell cracks. The severity of such faults depends on the degree and duration of the mismatch between current and voltage within the PV module [14]. When part of a PV array experiences HSFs, the irradiance reduces and the temperature increases. In a model, HSFs are simulated by adjusting the irradiance and temperature of specific modules. Figure 10 illustrates the I-V characteristics of an HSF. The HFS changes the slope of the I-V curve and decreases the open-circuit voltage of the PV array, while the short-circuit current remains nearly unchanged.

4. Example of Fault Diagnosis in a PV System

The I-V curve provides critical parameter information on the operational status of a PV array. A fault in a PV system changes the I-V curve with the type and severity of the fault and significantly influences the extent of these alterations. By comparing the I-V characteristics of a PV array under fault conditions and under normal conditions, potential faults are identified. The I-V curves of a PV system are simulated under various fault conditions using different models. Alternatively, after measuring the I-V curve with faults, curve fitting techniques are used to explore the I-V characteristics. These curves are compared with the I-V characteristics under normal conditions to diagnose the type and severity of the fault.

Figure 11 presents a diagnostic example based on I-V characteristics. After measuring the actual I-V curve at a PV site in this study, a curve was fitted. By comparing the model parameters and I-V characteristics of the curve under normal conditions, we diagnosed the fault type of the PV system. The array consisted of one string with 22 modules, with 3 sets of modules experiencing short-circuit faults and 19 sets showing degradation faults without open-circuit faults. Among the degraded modules, 10 sets displayed shading faults, with 9 sets experiencing a shading level of 11.1% and 1 set a level of 16.9%. The fault condition was diagnosed by curve fitting based on the I-V characteristics of the faults. SCFs were simulated to reduce the open-circuit voltage; module degradation was simulated by decreasing the shunt resistance (R_sh) or increasing the series resistance (R_s); and shading faults were simulated by reducing the irradiance of certain modules to increase the peak output. Then, the shading level was assessed based on the extent of the irradiance reduction.

Assuming that the three modules with short-circuit faults represented the worst conditions among the degraded modules, the results were simulated after repairing the short-circuit faults. The maximum output power of the PV array increased by approximately 7.7%.

5. Conclusions

The global installed PV capacity surpassed 1 TW in 2022, with this year earning the title “the year of terawatt solar”. With the rapid growth of the photovoltaic industry, fault detection and diagnosis techniques for various PV systems have been researched and developed. In particular, simple, low-cost, and effective detection methods based on I-V characteristic analysis have been used to assess the performance of PV systems. In this study, a practical fault diagnosis method was developed. Firstly, methods for detecting common faults in PV systems were reviewed using MATLAB/Simulink software. Module degradation, OCFs, SCFs, SFs, and HSFs were diagnosed, and the I-V characteristics of each fault were analyzed. Through curve fitting of the I-V characteristics of the faults, the fault diagnosis method was validated in terms of its effectiveness for identifying potential faults and assessing their severity.