Solar Cell Temperature Research Articles

Comparative numerical study of the energy performance of two different configurations of a hybrid photovoltaic/thermal air collector

Solar energy is a renewable alternative to fossil fuels. When solar energy is converted into electricity by photovoltaic (PV) solar modules, the heat generated increases the temperature of the PV solar cells and, as a result, reduces their electrical efficiency. Several techniques are used to cool PV solar cells in order to reduce their operating temperature. One technique is the use of hybrid photovoltaic/thermal (PVT) solar collectors, which simultaneously produce electricity and useful heat. In the airflow channel, the heat and mass transfer equations and those derived from the energy balance on the solid layers of the collector were discretized using the finite difference method with an implicit alternating directions (ADI) scheme. All the algebraic equations obtained after discretization were solved using the Thomas algorithm. The aim is to make a numerical comparison of the energy performance of the Verre-PV-Tedlar and Verre-PV-Verre flat-plate air PVT hybrid solar collectors in the climatic conditions of Lomé, Togo. Under the same operating conditions, the average electrical efficiencies of the Verre-PV-Verre and Verre-PV-Tedlar flat-plate air PVT hybrid collectors are 15.34% and 13.85% respectively. There is a 1.49% improvement in electrical efficiency for a Verre-PV-Verre air-source PVT hybrid collector compared with a Verre-PV-Tedlar PVT hybrid collector. The Verre-PV-Verre flat plate PVT air hybrid collector has an average daily electrical energy gain of 0.564 kWh and a thermal energy gain of 0.839 kWh, while the Verre-PV-Tedlar flat plate PVT air hybrid collector has an average daily electrical energy gain of 0.548 kWh and a thermal energy gain of 1.403 kWh.

Modelling and experimental investigation of cooling of field-operating PV panels using thermoelectric devices for enhanced power generation by industrial solar plants

The performance of commercial solar power plants degrades due to an increase in module temperatures for which standard PV-T air or water-cooling techniques are mostly used. In this study, a thermoelectric cooling system is studied for improving photovoltaic cell power efficiency and hence solar power generation. The cooling optimization requires solar cell temperature prediction of field operating PV modules, for which analysis of six models, is presented. The experimentation results show that TEC cooling maintains PV cell at 25 °C whereas PV cell without TEC operates at 55–63 °C, a higher temperature range, showing the effectiveness of the thermoelectric cooling system in precisely controlling PV cell temperature to operate at or near STC conditions in the field creating a temperature difference of 30–38 °C. The NOCT and Faiman model results are found close to the experimental values in comparison to other models. The potential for cooling and a corresponding increase in solar plant energy production is assessed using PV Syst modeling and simulation for three practical PV installation scenarios for 31 different climatic zone locations worldwide showing 6–27 % power loss due to elevated temperatures, which is not studied in previous studies adding novelty to the analysis. The results show that PV-TECS is an effective system to control the temperature of field operating PV modules, which can be used in future photovoltaic power plants. Field results and analysis of PV temperature models is crucial for the optimization and future development of PV-thermoelectric systems deployed under actual outdoor conditions as well as the expected cooling gains in different climatic locations. These aspects are collectively studied in the current work adding to the novelty of the study.

Performance evaluation of solar photovoltaic system based on perturb and observe (P&O) maximum power point tracking (MPPT) algorithm

The Photovoltaic system (PV) is gaining popularity as a future energy source due to its vast, secure, essentially limitless, and widely accessible nature. However, a prevalent challenge in PV systems is the impact of solar cell radiation intensity and temperature on the output power induced in the photovoltaic module. As a result, monitoring the input source's maximum power point becomes essential for maximizing the effectiveness of the renewable energy system. In order to harvest the most power from the photovoltaic system and channel it to the load through a boost converter, which raises the voltage to the necessary level, most Power Point Tracking, or MPPT, is essential. This study uses the Perturb and Observe (P&O) method to track the photovoltaic module's Maximum Power Point (MPP) and maximize the system's overall power production to overcome this obstacle. By varying the array terminal voltage or current, the P&O approach perturbs the system and then compares the PV output power to the preceding perturbation cycle. Therefore, the main goal of this study is to simulate and assess the effectiveness of the P&O MPPT algorithm in MATLAB/Simulink when it comes to maximizing power extraction from a photovoltaic system. The simulation findings show that the suggested P&O approach is a reliable means of tracking MPP from PV panels, even when there are fluctuations in solar irradiation.

Opto-electro-thermal simulation of heat transfer in monocrystalline silicon solar cells

AbstractIn the area of photovoltaics, monocrystalline silicon solar cells are ubiquitously utilized in buildings, commercial, defense, residential, space, and transportation applications throughout the world. Their performance is impeded by the heating of the cells during their interaction with the incident solar radiation. The development of reliable computer simulations that effectively model the thermal response of monocrystalline silicon solar cells is critical for their design, fabrication, and utilization. This work employs a novel computer simulation to incorporate the optical, electrical, and thermal properties of silicon in the thermal analysis of silicon solar cells. After establishing the theoretical principles and the values of these properties, the results of the simulation are compared with other established studies. The analysis shows that the percentage difference in solar cell temperatures between simulation and literature is within a range of 0.354–0.487%. The proposed simulation shows that the visible range of wavelengths is the dominant source of heating in commercial monocrystalline silicon solar cells.

Two-Phase Lattice Boltzmann Study on Heat Transfer and Flow Characteristics of Nanofluids in Solar Cell Cooling

During solar cell operation, most light energy converts to heat, raising the battery temperature and reducing photoelectric conversion efficiency. Thus, lowering the temperature of solar cells is essential. Nanofluids, with their superior heat transfer capabilities, present a potential solution to this issue. This study investigates the mechanism of enhanced heat transfer by nanofluids in two-dimensional rectangular microchannels using the two-phase lattice Boltzmann method. The results indicate a 3.53% to 22.40% increase in nanofluid heat transfer, with 0.67% to 6.24% attributed to nanoparticle–fluid interactions. As volume fraction (φ) increases and particle radius (R) decreases, the heat transfer capability of the nanofluid improves, while the frictional resistance is almost unaffected. Therefore, the performance evaluation criterion (PEC) of the nanofluid increases, reaching a maximum value of 1.225 at φ = 3% and R = 10 nm. This paper quantitatively analyzes the interaction forces and thermal physical parameters of nanofluids, providing insights into their heat transfer mechanisms. Additionally, the economic feasibility of nanofluids is examined, facilitating their practical application, particularly in solar cell cooling.

Enhancing Thermal Conductivity of Phase Change Materials Using Nanomaterials and its Effectiveness in Cooling Solar Cells

The latent heat storage system is considered the most promising technology in thermal energy storage (TES) because of its many advantages. This technique uses phase change material (PCM) as a TES medium. However, the low thermal conductivity (TC) of PCM is the major drawback of the system. Previous studies have shown that the addition of nanomaterials to PCM generally increases its TC. On the other hand, researchers tend to study the possibility of using enhanced PCM to cool solar cells. In fact, raising the solar cell’s operating temperature negatively impacts its efficiency. This problem is considered one of the biggest problems in solar energy systems, and researchers are still working to solve it. This paper focused on the possibility of enhancing the TC of paraffin by adding different shapes of silver nanomaterials to it (silver nanoparticles, silver nanowires and nanohybrid with silver nanoparticles and silver nanowires). Nanomaterials were added at volume fractions of 0.5% and 1%. Then, the study examined the ability to use this improved material to reduce the temperature of solar cells. We used the “Solidworks” program to create a 3D system, and then we used the “Ansys Fluent” software to simulate five cases; PV without PCM, PV with PCM, PV with PCM enhanced by nanoparticles, PV with PCM enhanced by nanowires, PV with PCM enhanced by nanohybrid (a mixture of nanoparticles and nanowires). The results show that using PCM enhanced by nanowire at a volume fraction of 0.5% gave the best results in decreasing the temperature of PV. The average temperature of PV declined by 14.9[Formula: see text]. In addition, the efficiency of PV rose by about 7.6%. Followed by using PCM enhanced by nanoparticles at a volume fraction of 1%. The average temperature of PV declined by 12.9[Formula: see text]. Moreover, the efficiency of PV rose by about 6.6%.

The potential of radiative cooling enhanced photovoltaic systems in China

Soaring solar cell temperature hindered photovoltaic (PV) efficiency, but a novel radiative cooling (RC) cover developed in this study offered a cost-effective solution. Using a randomly particle-doping structure, the radiative cooling cover achieved a high “sky window” emissivity of 95.3% while maintaining a high solar transmittance of 94.8%. The RC-PV system reached a peak power output of 147.6 W/m2. A field study to explore its potential in various provinces in China revealed significant efficiency improvements, with yearly electricity outputs surpassing those of ordinary PV systems by a relative improvement of 2.78%–3.72%. The largest increases were observed under clear skies and in dry, cool climates, highlighting the potential of RC-PV systems under real weather and environmental conditions. This work provided the theoretical foundation for designing scalable radiative cooling films for PV systems, unlocking the full potential of solar energy.

Modelling the Solar Cells Temperature and Power Output from PV Panels under Dusty Conditions

Abstract Almost all available power and temperature models of PV panels can only predict the power output and the cell temperature under clean conditions. The objective of this research is to study the effect of dust accumulation on the solar cells temperature as well as to model the solar cells temperature and the power output from PV panels under dusty conditions. An experimental setup has been developed in this study, which consists of a PV panel that contains two thermocouples embedded inside the panel, in order to measure the cells' temperatures. The cells temperatures, solar irradiance received by the panel and the power output from the panel have been measured for 5 consecutive weeks in Cairo, Egypt. The actual solar irradiance received by the cells, i.e. Gcell, was calculated using the power model of PV panels. Afterwards, the cell temperature was calculated based on the cells temperature model and the actual solar irradiance received by the cells. It has been found that the difference between the measured and the calculated cell temperature was not more than 3.71% during the time of experiments. Therefore, it can be concluded that the influence of dust on the power output as well as the cell temperature can be calculated using the power model and the cell temperature model under clean conditions, but the solar irradiance in these models should be replaced by the actual solar irradiance received by the cells, i.e. Gcell.

Exergetic and environment assessment of linear fresnel concentrating photovoltaic systems integrated with a porous-wall mini-channel heat sink: Outdoor experimental tests

The photovoltaic heat sink integrated systems can effectively control the temperature of solar cells and increase output power. The linear Fresnel concentrator photovoltaic heat sink integrated systems (LFC-PV systems) consists of a solar cell, a porous-wall mini-channel heat sink (MFP-channel) and a linear Fresnel concentrator (LFC) mirror array. Cooling fluid R141b absorbs heat from solar cell back by flow boiling heat transfer. In this work, we tested the system performance outdoors and the collected experimental data were used for energy analysis, exergy analysis and environmental analysis to evaluate the advantages and disadvantages of the integrated system. The experimental data show that the heat dissipation of solar cells by using a porous-wall mini-channel heat sink can not only well control the surface temperature of solar cells between 35 °C and 40 °C, but also increase the output power and exergy, where the output power can reach a maximum of 10.73 W. Therefore, it is recommended to use a porous-wall mini-channel heat sink for the thermal management of the LFC photovoltaic system.

Examining the Application of Neural Networks in Predicting Temperature and Solar Cell Power

Cost-effective and efficient engineering of solar cells, in addition to creative design, necessitates a detailed examination of the physics involved in solar energy absorption. Solar energy does not operate at night without storage means such as batteries, and cloudy weather can render this technology unreliable during the day. Since solar cells are used to convert light into electricity, they must be composed of materials efficient in light absorption. Like any other power generation source, solar cells face challenges, including reduced output power with increasing cell surface temperature. With each degree rise in temperature, efficiency can decrease by up to 54.0%, highlighting the importance of addressing and implementing solutions to this issue. This study explores different specifications of solar cells after a general overview and modeling. It investigates methods for estimating solar cell temperature using ambient temperature and solar radiation, comparing them with a proposed neural network approach. If the neural network can achieve lower error rates than the desired thresholds, it would offer advantages over mathematical estimators mentioned in the literature. Additionally, this neural network demonstrates the capability to predict future solar cell temperatures, unlike instantaneous mathematical estimators for temperature and radiation. DOI: https://doi.org/10.52783/pst.558

Designing an absorber for solar photovoltaic thermal systems: a heat pipe approach and comparative evaluation

Abstract Renewable energy power generation is extensively promoting for the global objective of mitigating greenhouse gas emissions. Solar energy leads all renewables due to its ubiquitous nature which can be harvested by solar thermal and solar photovoltaic devices. Solar PV is used widely because of its flexibility in working with both direct and diffuse radiation. However, PV systems experience efficiency loss for every degree rise of solar cell temperature above room temperature. Solar photovoltaic-thermal (PVT) collectors are proposed for co-generation of electrical and thermal energy by utilizing excess heat generated in the PV layer. The low heat transfer rate, energy-intensive nature, and freezing of working fluid in colder regions are some problems that persist in water- and air-based PV/Ts, leading us to consider alternatives. Thus, the present study addresses the current opportunities by utilizing heat pipes as PV/T absorbers. Heat pipe PVT is a passive system which operates in phase transition facilitating a tremendous amount of heat transfer enhancement. To enhance the efficiency of PV/T systems, the integration of advanced technologies, like heat pipe thermal absorbers, becomes imperative. Incorporating cutting-edge solutions, such as heat pipe thermal absorbers, is essential to optimize the performance of PV/T systems. Hence, the current study provides insight into heat pipe design through a case study on the application of heat pipes as thermal absorbers for photovoltaic systems. The existing absorbers with fluid flow configurations like Raster, Spiral or Rectangular spiral, etc. used in PV/T are considered as reference. Copper as tube material and water as working fluid are found to be compatible with each other for the proposed heat pipe. The maximum heat input of 0.55kW is computed by the inadequacy of the mentioned existing PVTs to extract the maximum available heat from the PV surface. Additionally, an analytical approach used to define the optimum size of the system and calculation of minimum requirement of tubes. The optimized design of copper heat pipes was found to be ½ inch in diameter and at least ten heat pipes were required to transfer a computed heat flux of 466kW/m 2 per pipe to outperform the existing PV/Ts.

MPPT Charge Controller using Fuzzy Logic for Battery Integrated with Solar Photovoltaic System

In comparison to other Renewable Energy (RE) resources, solar energy has become the most prominent and prospective source for generating electricity, substituting conventional sources. However, solar Photovoltaic (PV) energy production is dependent on solar irradiance and cell temperature. By implementing the Maximum Power Point Tracking (MPPT) algorithm, it is achievable to maximize the power from solar PV. In spite of this, there is still a slower convergence rate, a significant fluctuation around Maximum Power Point (MPP), and a drift issue caused by rapid irradiance variations in solar PV. In order to prevent oscillation and attain a steady state and continuous output of the PV module, a Fuzzy Logic (FL)-based MPPT has been designed in this work. With the buck converter as the DC-DC converter and the lead acid battery as the input, the Perturb & Observe (P&O) MPPT method is selected. The overall design will be developed using Matlab Simulink, and the efficiency of the FL-MPPT charge controller will be evaluated under constant and step irradiance. Additionally, the battery's State of Charge (SOC) will be monitored to prevent overcharging and discharge. In addition, the effectiveness of the controller will be evaluated with and without the MPPT method. On the basis of simulation results obtained from constant and step irradiance levels, the FL-MPPT charge controller with the P&O algorithm and the lead acid battery as the load was able to maintain maximum system efficiency while extending battery life. The FL-MPPT charge controller obtained about 96% efficiency for both irradiance profiles, whereas the system without the FL-MPPT algorithm only achieved 42% efficiency.

The Performances of Use and Non-Use Maximum Power Point Tracking for Photovoltaic Systems

Solar arrays are nonlinear devices, and as a result the output current-voltage are dependent on their bias point. Power can be optimized by biasing the PV cell’s voltage at the maximum power point (MPP). The PV array is affected by solar irradiation, cell temperature, age, loading condition, and other conditions. These variations are also having nonlinear responses, which need to control with the help of buck boost converter to ensure that maximum power output is transferred to the load. This paper summarizes some of important applications of the MPPT. A comparison between direct and indirect connected to resistive load with MPPT in terms of PV total energy. The MPPT algorithm, which is based on the perturb and observe method) plays an important function that is developed in MATLAB Simulation, and the outputs are observed. It includes module BP SX 150S for a solar photovoltaic. This module approximately provides a maximum power of 150 W.

Design and investigation of high power quality PV fed DC-DC boost converter

This study presents a new improved voltage gain dc-dc converter architecture to maximize solar photovoltaic (PV) power output. The maximum power point tracking (MPPT) method utilizes particle swarm optimization (PSO)–based artificial neural networks (ANN) to reduce the oscillations of output electrical performance at the maximum power point (MPP). For any solar cell temperature and irradiance, the ANN delivers the electrical current and voltage outputs at the MPP and thus could reach the MPP in minimum instances. A DC-DC converter needs specific characteristics to work with photovoltaic systems. These include higher voltage development to meet increased DC link voltage requirements, the continuous input current to extend the PV system life, common grounding to prevent electromagnetic interference, and reduced electrical stress with fewer components. This paper combines a quasi-switched switch inductor network and extendable diode capacitor modules to achieve the quality mentioned earlier. The converter offers constant input current, high efficiency (95 %) with considerable gain (12 times higher than input), lower voltage stress (almost one-fifth of output voltage), and a common grounding feature. The effectiveness of the proposed MPPT technique is analyzed in terms of higher efficiency, MPP tracking time, gain, and the normalized voltage stress on diodes. The experimental step is developed to validate the simulation (Simulink and Psim) results of the present research work.

Enhancing photovoltaic system maximum power point tracking with fuzzy logic-based perturb and observe method

Photovoltaic systems have emerged as a promising energy resource that caters to the future needs of society, owing to their renewable, inexhaustible, and cost-free nature. The power output of these systems relies on solar cell radiation and temperature. In order to mitigate the dependence on atmospheric conditions and enhance power tracking, a conventional approach has been improved by integrating various methods. To optimize the generation of electricity from solar systems, the maximum power point tracking (MPPT) technique is employed. To overcome limitations such as steady-state voltage oscillations and improve transient response, two traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb and observe (P&O), have been modified. This research paper aims to simulate and validate the step size of the proposed modified P&O and FLC techniques within the MPPT algorithm using MATLAB/Simulink for efficient power tracking in photovoltaic systems.

Evaluation of Optimum MPPT Controllers for PV Cells using Matlab Simulink Platform

Abstract: Nowadays, the Photovoltaic cell is one of the most essential parts in the electrical field to convert photo light to voltage and current, at the desired output voltage and frequency by using various control techniques The Solar panel can produce maximum power at a particular operating point called Maximum Power Point (MPP).To produce maximum power and to get maximum efficiency, the entire photovoltaic panel must operate at this particular point. Maximum power point of PV panel keeps on changing with changing environmental conditions such as solar irradiance and cell temperature. Thus to extract maximum available power from a PV module, MPPT algorithms are implemented. The output power of a photovoltaic (PV) module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement MPPT controllers to obtain the maximum power of a PV system regardless of variations in climatic conditions. This project reviews the most used MPPT algorithms, which are Perturb and observe (P&O), Incremental conductance method (ICM), and Fuzzy logic control (FLC).

Adaptive Particle Swarm Optimization based improved modeling of Solar Photovoltaic module for parameter determination

Solar Photovoltaic (SPV) panel manufacturers provide a datasheet, which contains key electrical specifications of the SPV module for the user's reference. However, these data alone prove inadequate in anticipating the performance of PV systems under fluctuating atmospheric conditions. The approach put forth in this paper ascertains five parameters of a PV module using information supplied by the manufacturer. This approach considers the impact of solar radiation and cell temperature. The paper outlines the process for determining the parameters of the five-parameter model and establishes a comparison between projected current-voltage curves and manufacturer-provided data. To extract the unknown parameters of a single diode model Adaptive Particle Swarm Optimization (APSO) techniques with barrier constraints was developed. The study investigates both multi-crystalline and mono-crystalline PV module technologies to validate the efficacy of the proposed method. Results demonstrate that the proposed technique, besides being straightforward, proves adept at accurately simulating the dependable performances of PV modules. Comparative analysis reveals that the proposed methods extract parameters with minimal modeling errors and high precision, regardless of temperature fluctuations, outperforming conventional PSO methods.

A Hybrid Experimental Modelling Approach to Solar Photovoltaic Cell Temperature Prediction

Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting especially in commercial power plants. The present study focused on the development of a machine learning based predictive model for solar photovoltaic cell temperature prediction in commercial solar photovoltaic power plants. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for those parameters difficult to measure experimentally and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed for each parameter used to mimic experimentally measured data. Statistical approaches were adopted to analyse the influence of both the dependent and independent variables on solar cell temperature. The analysis included multicollinearity and correlation analysis with the aid of heat maps meant to establish the relationships among the independent and dependent variables. Feature selection and dimensionality reduction was also performed to reduce the input variables and maintain relevant data in the modelling process. Parameters with a strong correlation to each other had some of them eliminated in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.83 °C while an R2 value of 0.93 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed a 3.4% difference in the annual energy generated between a simulation which considered solar cell temperature and that which ignored the solar cell temperature. The study also revealed that this difference is even larger in monetary terms when lifetime energy generation is considered. The present study demonstrated that Random Forests, a machine learning approach to predictive modelling, can handle complex models and can provide models with a higher accuracy compared to statistical modelling approaches. The study recommends the use of solar cell predictive models to improve the accuracy of energy prediction on solar photovoltaic power plants which in turn assists in energy planning and deployment.

Maximum power point tracking using unsupervised learning for photovoltaic power systems

ABSTRACT The Maximum Power Point Tracking (MPPT) technique in the solar energy field optimises the performance of solar panels in different atmospheric conditions and variable loads. In this study, we present a new method that uses unsupervised learning (K-means clustering) to identify the atmospheric clusters of solar irradiance and cell temperature (G, T) and delimit homogeneous atmospheric zones or clusters to reduce the search space of the optimal parameters ( V mp , I mp , and P mp ). The data collected for one year is segregated into 12 clusters; in every cluster, 04 regions are defined based on every cluster’s centroid ( G c , T c ). A local search of the reference voltage/Duty cycle per cluster region is initiated for every sensed (G, T). Variable atmospheric conditions and resistive loads are tested. The results show that the efficiency of the DC/DC converter is 97.5% with a settling time (4.013 ms/5.577 ms) compared to the Perturb and Observe (P&O) the conventional tracking method and the Particle Swarm Optimisation (PSO), both applied locally inside a cluster and a deviation of 2% from the global maximum.

A proposed hybrid model of ANN and KNN for solar cell defects detection and temperature prediction using fuzzy image segmentation

This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar cells. Focusing on issues such as broken corners and black edges caused by environmental factors like broken glass cover, dust, and temperature variations. This study utilizes a hybrid model of ANN and K-Nearest Neighbor (KNN) for temperature prediction. This hybrid approach leverages the strengths of both models, potentially opening up new avenues for improved accuracy in temperature forecasting, which is critical for solar energy applications. The significance lies in the interconnectedness of temperature fluctuations and solar cell efficiency, leading to defects. The proposed model aims to predict temperatures accurately, providing insights into potential solar cell efficiency problems. Subsequently, this work studies the transitions to defect detection using Fuzzy C-Means (FCM) clustering and MM techniques. The hybrid model demonstrates accurate temperature prediction with Mean Absolute Percentage Error (MAPE) values of 0.92 %, 0.72 %, and 1.3 % for average, maximum, and minimum temperatures, respectively. The defect detection process yields a detection accuracy (CR) of 96 % and sensitivity of detection (SD) of 89 %. This work is validated compared to the literature work done and by using K-fold cross validation technique. The proposed work emphasizes the improvement in defect detection accuracy and the overall quality enhancement of solar cells.