Characteristics Of Photovoltaic Panel Research Articles

Combined multi-level context aggregation and attention mechanism method for photovoltaic panel extraction from high resolution remote sensing images

ABSTRACT In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Using high-resolution remote sensing images to accurately obtain PV information over a large region, including location and size, has the advantages of high statistical efficiency and timely data update for the PV energy management. Due to the intra-class diversity of PV panels and the intricate variability in their deployment environments, existing semantic segmentation methods often have problems such as under-segmentation and mis-segmentation. To alleviate these problems, this paper proposes an improved DeepLabv3+ semantic segmentation network to more accurately extract PV panels from high-resolution remote sensing images. With the aim of alleviating under-segmentation, a multi-level context aggregation module is developed. This module can enhance the model’s ability to learn the characteristics of PV panels and their surrounding environment by aggregating rich contextual information from multi-scale and semantic levels. To alleviate the problem of mis-segmentation, a hybrid attention module is introduced. This module sequentially and adaptively adjusts the weight distribution in both the channel and spatial dimensions, thus enabling the model to focus more on the feature information and spatial positions of PV objects. Experiments conducted on a self-constructed Beijing PV segmentation dataset show that the method in this paper has advantages of completeness and accuracy in extracting PV panels compared to the baseline model and current mainstream semantic segmentation network. In addition, the results of experiments on extracting PV panels in real region show that our model also has good stability and generalization capability.

A Method for Extracting Photovoltaic Panels from High-Resolution Optical Remote Sensing Images Guided by Prior Knowledge

The extraction of photovoltaic (PV) panels from remote sensing images is of great significance for estimating the power generation of solar photovoltaic systems and informing government decisions. The implementation of existing methods often struggles with complex background interference and confusion between the background and the PV panels. As a result, the completeness and edge clarity of PV panel extraction results are compromised. Moreover, most previous studies have overlooked the unique color characteristics of PV panels. To alleviate these deficiencies and limitations, a method for extracting photovoltaic panels from high-resolution optical remote sensing images guided by prior knowledge (PKGPVN) is proposed. Firstly, aiming to address the problems related to missed extractions and background misjudgments, a Photovoltaic Index (PVI) based on visible images in the three-band is constructed to serve as prior knowledge to differentiate between PV panels and non-PV panels. Secondly, in order to strengthen information interaction between shallow features and deep features and enhance the accuracy and integrity of results, a Residual Convolution Hybrid Attention Module (RCHAM) is introduced into the skip-connection of the encoding–decoding structure. Finally, for the purpose of reducing the phenomenon of blurred edges, a multilevel Feature Loss (FL) function is designed to monitor the prediction results at different scales. Comparative experiments are conducted with seven methods, including U-Net, on publicly available datasets. The experimental results show that our PKGPVN achieves superior performance in terms of evaluation metrics such as IoU (above 82%), Precision (above 91%), Recall (above 89%), and F1-score (above 90%) on the AIR-PV dataset. Additionally, the ablation experiments illustrate the effectiveness of our key parts. The proposed method reduces the phenomena of missed extractions and background misjudgments effectively while producing highly accurate results with clear boundaries.

Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets

The widespread adoption of photovoltaic (PV) technology for renewable energy necessitates accurate segmentation of PV panels to estimate installation capacity. However, achieving highly efficient and precise segmentation methods remains a pressing challenge. Recent advancements in artificial intelligence and remote sensing techniques have shown promise in PV segmentation. Nevertheless, real-world scenarios introduce complexities such as diverse sensing platforms, sensors, panel categories, and testing regions. These factors contribute to resolution, size, and foreground-background class imbalances, impeding accurate and generalized PV panel segmentation over large areas. To address these challenges, we propose GenPV, a deep learning model that leverages data distribution analysis and PV panel characteristics to enhance segmentation accuracy and generalization. GenPV employs a multi-scale feature learning approach, utilizing an enhanced feature pyramid network to fuse data features from multiple resolutions, effectively addressing resolution imbalance. Moreover, inductive learning is employed through a multitask approach, facilitating the detection and identification of both small and large-sized PV panels to mitigate size imbalance. To address significant class imbalance in PV panel recognition tasks, we integrate the Focal loss function for effective hard sample mining. Through experimental evaluation conducted in Heilbronn, Germany, our proposed method demonstrates superior performance compared to state-of-the-art approaches in PV panel segmentation. The results exhibit progressively higher accuracy and improved generalization capability. These findings highlight the potential of our method to serve as an advanced and practical tool for PV segmentation in the renewable energy field.

Predictive modeling of PV solar power plant efficiency considering weather conditions: A comparative analysis of artificial neural networks and multiple linear regression

This study investigates the surface parameters and environmental factors affecting the energy production of a 500 kWp photovoltaic (PV) solar power plant in Igdir province. Using both the PV panel characteristics and the weather conditions specific to the power plant location, a total of 7 detailed features were included. The estimation of the power plant efficiency, a novel contribution not found in previous studies, is also a major focus. The performance evaluation of different models, including feed-forward neural networks and multiple linear regression, demonstrates the effectiveness of artificial neural networks in capturing the complex relationships between features and efficiency despite limited data availability. Principal Component Analysis (PCA) was used to reduce feature dimensions, showing that even with a reduced feature set, accurate efficiency prediction is still achievable. Prediction using PCA is one of the novelties of the paper. The effects of solar irradiation, module power, and module temperature on power plant efficiency are revealed. The results provide valuable insights for optimizing energy investments in the Igdir region and highlight the potential of artificial neural networks in energy forecasting, demonstrating their suitability for capturing complex patterns in solar power plant efficiency.

A physical model with meteorological forecasting for hourly rooftop photovoltaic power prediction

Accurate photovoltaic power forecasting provides essential information for the flexible control of building energy systems. This paper proposes a physical model with environmental parameter prediction and an improved maximum power point tracking algorithm for hourly photovoltaic power forecasting. This study incorporates the forecast of meteorological parameters into the photovoltaic model so that the physical model proposed in this paper can achieve photovoltaic power prediction in the face of different weather conditions compared to previous photovoltaic physical models in the literature. A hierarchical clustering algorithm was used to obtain future hourly irradiance based on different weather conditions. The coordinate analysis method was used to calculate the hourly irradiance received on the surface of the photovoltaic panel. An equivalent circuit model was used to calculate the current-voltage characteristics of photovoltaic panels. Finally, the output voltage of the photovoltaic panel was adjusted by the improved maximum power point tracking algorithm to obtain the photovoltaic power. This algorithm can accelerate the calculation process and avoid long convergence times or oscillations near the optimal value. A photovoltaic project located in central China was selected as a case study to verify the accuracy of the prediction model. The long-term monitoring results show that the relative error of the predicted irradiance of the photovoltaic panel surface is 18.5%. The mean absolute error of the forecasted photovoltaic power was 15.9% in 120 consecutive days under various weather conditions, indicating that the model had high accuracy compared with traditional machine learning methods.

Dust deposition mechanism and output characteristics of solar bifacial PV panels.

The utilization of solar photovoltaic (PV) power generation represents a highly promising technological solution for addressing environmental challenges and energy crises. Dust deposition on the front and back surfaces of solar bifacial PV panels greatly decreases the optical performance and power generation. In this study, the dust deposition characteristics and mechanism of solar bifacial PV panels are investigated using the CFD-DEM method. The effects of the dust deposition rate on the output characteristics of bifacial PV panels are discussed. The research results show that the particle deposition behaviors on the back and front surfaces of bifacial PV panels are influenced by the deposition and separation forces at the left or right inlets. The dust deposition rate of windward surfaces can be 1.48-7.60 times that of the leeward surfaces of bifacial PV panels. The particle motion trajectories on the windward and leeward sides can be mainly divided into five and three kinds, respectively. The dust deposition rate of bifacial PV panels increases when the air inlet velocity decreases and the particle size and concentration and relative humidity increase. The open circuit voltage and short circuit current of bifacial PV panels decreased by 26.7% and 16.4%, respectively, when the dust deposition rate increases by 45.8%. The attenuation rate of the maximum output power of PV panels has a positive linear correlation with the dust deposition rate, as shown in Eq. 22. The bifacial PV panels have better output characteristics than the mono-facial PV panels with consideration of dust deposition.

Hydrogen Production System Using Alkaline Water Electrolysis Adapting to Fast Fluctuating Photovoltaic Power

Using photovoltaic (PV) energy to produce hydrogen through water electrolysis is an environmentally friendly approach that results in no contamination, making hydrogen a completely clean energy source. Alkaline water electrolysis (AWE) is an excellent method of hydrogen production due to its long service life, low cost, and high reliability. However, the fast fluctuations of photovoltaic power cannot integrate well with alkaline water electrolyzers. As a solution to the issues caused by the fluctuating power, a hydrogen production system comprising a photovoltaic array, a battery, and an alkaline electrolyzer, along with an electrical control strategy and energy management strategy is proposed. The energy management strategy takes into account the predicted PV power for the upcoming hour and determines the power flow accordingly. By analyzing the characteristics of PV panels and alkaline water electrolyzers and imposing the proposed strategy, this system offers an effective means of producing hydrogen while minimizing energy consumption and reducing damage to the electrolyzer. The proposed strategy has been validated under various scenarios through simulations. In addition, the system’s robustness was demonstrated by its ability to perform well despite inaccuracies in the predicted PV power.

Analysis and Evaluation of Photovoltaic Cell Defects and Their Impact on Electricity Generation

Many problems arise in the operation of photovoltaic systems. Each of these problems affects the operation of photovoltaic systems by reducing the power of the entire system. Some problems can be avoided during the design of photovoltaic systems. For example, when designing photovoltaic systems, it is possible to eliminate the shading of photovoltaic panels from surrounding objects. It is also necessary to look at the shading from neighboring photovoltaic panels when designing photovoltaic systems. It is necessary to calculate the inclination of the sunlight based on the position of the sun and calculate the distance between the two panels accordingly. However, some problems appear during the operation of photovoltaic systems. These problems can be partially eliminated. The magnitude of the series resistance can be eliminated by reducing the transition resistance throughout the system. The paper describes these problems and their possible solutions in practice. In the practical part, we focused on simulations and experiments in the field of photovoltaic systems (PV). The simulations focused on both the influence of temperature and the series resistance of photovoltaic panels on the production of electricity. The experiments were focused on the influence of various faults/defects on the power and V-A characteristics of photovoltaic panels connected in strings. The paper also discusses the impact of bypass diodes on the operation of photovoltaic systems and determines the need to use bypass diodes.

A critical evaluation of maximum power point tracking techniques for PV systems working under partial shading conditions

Photovoltaic (PV) energy is a promising source of renewable energy which is sturdy and environmentally friendly. PV generation systems, once installed, produce electricity from solar irradiance without emitting greenhouse gases. To maximize the output power of PV systems, the maximum power point tracking system has been employed (MPPT). The MPPT constitutes a fundamental part of PV systems. In recent years, a large number of MPPT techniques have been proposed. This paper is set up to critically review some of the proposed maximum power point tracking (MPPT) techniques to handle the emergence of multiple MPPs in PV panel characteristics due to the partial shading conditions (PSCs). To define the working principle and the pros and cons of the different proposed techniques clearly and sequentially, they are divided into three groups as follows: conventional MPPT techniques, improved MPPT techniques and artificial intelligence- based MPPT techniques to deal with PSCs. The paper also critically summarizes the findings in terms of their performance in capturing the global maximum power point (GMPP) for PV systems operating under PSCs.

Power optimization of a photovoltaic system with artificial intelligence algorithms over two seasons in tropical area

Power output from the PV module changes continuously with time depending upon the climatic condition. This changes are most important in tropical area like Senegal due to the variation of the seasons (dry and rainy). Furthermore, different types of maximum power point tracking (MPPT) algorithm are presented in literature in order to get maximum output from the PV system. They can be summarized in two categories: classical and intelligent methods. The classical methods in no uniform weather condition are not efficient and an important loss of energy is showed. However, faced to this problematics like energy loss and no uniform weather conditions an Adaptative methods is used to optimize the PVs energy. In this study, two intelligent controllers based on artificial neural networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) are proposed to optimize the PVs production in non-uniform weather conditions and compared in order to show the most powerful model. For the ANN, the main challenge is to find the optimal neural in the hidden layer and in the paper, it is obtained using evaluator factor like mean squared error (MSE). These techniques using artificial intelligence (AI) algorithms are used for power optimization of a photovoltaic system are trained and validated with real data from a photovoltaic micro power plant in dry and rainy season installed at polytechnic high school of Dakar. The performances of the controllers to optimize the PVs power are evaluated during the dry and rainy seasons. Simulation results show that the ANFIS MPPT controller is more efficient and robust than ANN in non-uniform weather conditions. They have the ability of generalization and adaption to each meteorological conditions. These bullet summarize the applied methodology•A real electrical characteristics of photovoltaic panel are used for learning and validation of the controllers.•A comparative study of the methods in two different season is done.•ANFIS gives best performance in weather conditions compared to the ANN.

Location of Photovoltaic Panels in the Building Envelope in Terms of Fire Safety

Abstract The increasing amount of energy consumed by the operation of buildings results in an increasing environmental burden. This has a direct impact on the innovation of renewable energy technology and its application in building industry. In accordance with the requirements of the “Europe 2020 Strategy” and “Europe 2030 Project”, the long-term goal is to achieve the maximum use of renewable energy sources in the buildings´ operation. One of the solutions is the building-integrated photovoltaic power plant. The paper analyses efficiency and incorporation of photovoltaic (PV) systems into the roofs and perimeter walls. PV systems are a renewable energy source used in the construction of zero energy buildings producing minimal environmental pollution. Semi-transparent building-integrated photovoltaics (BIPV) are one of the technologies that have the potential to increase the building´s energy efficiency as well as aesthetically complete the building´s design. The paper deals with material characteristics of PV panels in terms of the fire risk and definition of the fire hazardous area that arises during their combustion. It is focused on the incorporation of various types of PV technology into the facades and roofs, structural and material solutions of fire-dividing structures to which PV systems are fastened considering their location in the fire hazardous area. Based on the external fire simulations, conclusions in this paper bring the optimal position of PV systems in the building envelope in relation to fire open areas, fire lines and fire hazardous areas. The safety of firefighters during the intervention also depends on the position of PV systems. The case study presented in this paper uses a model solution to give the optimal position of PV systems in the building envelope considering the fire safety.

Development of a Technique for Classifying Photovoltaic Panels Using Sentinel-1 and Machine Learning

With the increasing interest in effective renewable alternative energy sources resulting from the Paris Agreement on Climate Change in 2015, photovoltaic (PV) power generation is attracting attention as a practical measure. In this study, we develop procedures for efficiently monitoring PV panels in a large area and increasing their classification accuracy to enable efficient management of PV panels, an important component of renewable energy generation. To accomplish this, first, the persistent scatterer characteristics (e.g., polarization, imaging module, and topography) of PV panels in SAR images were utilized. Then, we developed a technique for classifying panels over a certain size using the polarization and pulse-scattering characteristics of Sentinel-1. Next, by stacking Sentinel-1 ground range Doppler (GRD) images and comparing them with the surroundings of the same area, the morphological features of PV panels were derived and built as learning data for machine learning. Then, a more precise classification of PV panels was performed by applying these learning data in AI algorithms. When SAR-based AI training data for the same PV panels were used in the YOLOv3 and YOLOv5 algorithms, both algorithms showed high accuracy of over 90%, but there were differences in precision and recall. These findings will enable more efficient monitoring of PV panels, the use of which is expected to increase in the future. In addition, they can serve as a proactive response tool to address environmental problems such as PV panel waste and panels washed away during natural disasters.

A novel image enhancement algorithm to determine the dust level on photovoltaic (PV) panels

An accurate evaluation of the dust accumulation on photovoltaic (PV) panels enables the development of cleaning plans and improves the grid connection security of PV power stations. In this paper, a novel image enhancement algorithm is proposed to evaluate the dust accumulation on PV panels. An atmospheric scattering model was used to analyze the difference in the image characteristics of clean and dusty PV panels. A model was proposed to describe the relationship between the model coefficient and the dust level by minimizing the pixel difference between the images of the soiled and clean PV panels. An experiment system was designed to acquire the images of clean and soiled PV panels with different dust levels. The model parameters were determined by data fitting, and the model’s generalization ability was improved by adding a noise item. The results show that the model can estimate the dust level on PV panels with an accuracy of 83.78%. The average root mean square error (RMSE) of samples 1–4 is 1.67, and the error range is 2–3.5 g/m2. In addition, the proposed method has higher accuracy than the optical attenuation and weighing methods, with a mean absolute error (MAE) of 1.61 g/m2. The method is simple and reliable for intelligent cleaning of PV panels.

Fractal textured glass surface for enhanced performance and self-cleaning characteristics of photovoltaic panels

Photovoltaic (PV) modules face significant performance loss due to the reflection of solar radiation and dust accumulation on the PV glass cover. Micro- and nanoscale texturing of the PV panel glass cover is an effective means of reducing solar radiation reflection and providing surface hydrophobicity to reduce dust accumulation and ease cleaning. Considering multiscale surface texturing of PV panel glass cover, represented generically as fractal surfaces, the study comprehensively investigates the effect of texturing on the module’s optical, thermal, and overall performance, as well as the surface’s wettability characteristics. Numerical simulations over a range of fractal texturing parameters and geographical locations indicate that texturing reduces reflectance by over an order of magnitude and boosts the power output by as much as over 4 % depending on the fractal parameters, which is within 0.2 % of the theoretical maximum for an ideal case of zero reflectance on the glass cover. Complementing the power production enhancement, texturing is also shown to impart hydrophobicity or superhydrophobicity, which is essential for the surfaces to resist or easily remove soiling and dust accumulation. A design map for tailoring the fractal texture parameters so as to achieve the desired self-cleaning characteristic is derived from the study. The article elucidates, for the first time, a systematic fundamental understanding of texturing to enhance PV panel performance and provides information for designing texturing parameters for desired performance enhancement.

Advanced Maximum Power Point Tracker for PV Systems Under Dusty Weather Environments

Dusty weather is a most important disturbance for photovoltaic (PV) systems, that may result in PV panels exposure to partial shading conditions where the PV panel characteristics show several maximum power points. Moreover, dusty weather conditions may lead to rapid changes in solar radiations. Thus, conventional power system tracking algorithms are not being capable to track the global maximum power point (GMPP). This paper provides an advanced maximum power point tracker for partial shading and dusty weather conditions. In the presented technique, scanning and power lock conditions are added to the conventional perturbations and observation technique in photovoltaic system under partial shading or dusty weather conditions. This tracking method scans the PV characteristic in steps and stores all the maximum power points in order to get the GMPP. The proposed tracker is capable to identify the uniform weather conditions to save it from having unnecessary scanning. In addition, this tracker permits dynamic tracking of the GMPP under dusty weather conditions with less fluctuations and stable operation compared to the conventional methods. Accurate and fast conversion to the GMPP during various weather conditions is accomplished using the proposed technique. The proposed modified perturb and observe technique is validated using simulation and practical results. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Implementation of Simple Low Cost PV Panel Characterization Kit using Arduino

Temperature, solar irradiation, direction and spectrum of sunlight always has influence on the generation of power in photovoltaic (PV) system. Temperature and irradiation are erratic in nature. It affects the efficiency of PV module. In view of module’s low efficiency, it is prudent to work the module at peak power point. By that maximum power can be delivered to the load under waffling temperatures and solar radiations. The PV panel characteristics are known under the standard test conditions (STC). In practical situations, the PV characteristics are undisclosed out of STC. Therefore, the measurement of PV peculiarities such as voltage, current and power are required in real-time. This paper adducing a low cost PV characterization kit which is based on Arduino UNO and Excel. It is used to monitor the characteristics on PV side as well as load which is connected to source through maximum power point tracker. A simulation study of the MPPT with perturb and observe algorithm is carried out in MatLab/Simulink. Hardware is built to validate the simulation results

Assessing the Techno-Economic Impact of Derating Factors on Optimally Tilted Grid-Tied Photovoltaic Systems

Photovoltaic (PV) systems encounter substantial losses throughout their lifespan due to the different derating factors of PV modules. Those factors mainly vary according to the geographical location and PV panel characteristics. However, the available literature does not explicitly concentrate on the technical and economic impact of the derating factors within the PV system. Owing to that necessity, this study performs a comprehensive analysis of various PV loss parameters followed by a techno-economic assessment of derating factors using the average value on a grid-connected and optimally tilted PV system located in Hatiya, Bangladesh. Some criteria linked to the derating factors such as PV degradation and ambient temperature are further explored to analyze their impact on the aforementioned power system. Simulation results show that PV power generation would vary around 12% annually, subject to a 10% variation in the derating factor. Again, a 10% difference in the derating factor changes the net present cost (NPC) by around 3% to 4%. The system provides the best technical performance concerning annual PV production, power trade with the grid, and the renewable fraction at a higher value of the derating factor since it represents a lower impact of the loss parameters. Similarly, the financial performance in terms of the NPC, levelized cost of energy (LCOE), and grid power exchange cost is found to be lower when the derating factor value is higher.

Simple PV Modeling Under Variable Operating Conditions

Photovoltaic (PV) panels are increasingly used to convert sunlight into electricity, as a source of sustainable energy. It can be used in a wide variety of applications ranging from the well-known power generation to the emerging energy harvesting in Internet of Things (IoT). Hence, an accurate model is required to evaluate and predict the performance of the PV panel. However, the non-linear characteristics of PV panels make the modeling of their electrical response a challenging task. In the literature, most of the previous PV models have been developed for large wattage PVs under high irradiance, or for small wattage PVs under lower irradiance. Those that can model both usually require more information, including the I-V curves data at different irradiances, which is not always provided by the manufacturers. Therefore, this paper presents a simple PV modeling that can be applied for different wattage panels at different operating levels of irradiance, using only the commonly provided datasheet values at standard test condition (STC). The model uses the characteristic points translation technique to translate the short circuit current, open circuit voltage and maximum power voltage points, at STC to other operating conditions. These translated values are then used by the parameter extraction technique to extract the model's parameters. The proposed model's techniques can model the losses across the resistors at low irradiance, which reduces the error. The accuracy of the proposed model is validated using two representative commercial PV panels. Results are generated for the proposed model and other comparative works. The results show that the proposed model can improve the accuracy over the other compared works, with a consistent percentage difference of below 5% across all levels of irradiances for both panels.

Experimental trial on the electrical characteristics of photovoltaic panels under the effect of a simulated failure

In this work we measure electrical characteristics of different panels following the addition of a simulated failure, namely cracks on the protective layer of photovoltaic cells. The objective is to see its influence on the electrical production of panels on one side, and to see if it is possible to diagnose this failure on the other. The cracks are simulated using broken transparent plates that will be positioned on each solar panel before measuring its real characteristics. The results show that despite the continuity of the operation of the photovoltaic equipment in the presence of cracks, those last clearly influence the electrical power supplied by the panel. Among other things, they give a very particular form to its I-V characteristic which can be used to detect this failure.

Моделювання роботи фотоелектричних панелей з використанням середовища Matlab/Simulink

Modeling of the photovoltaic panels’ parameters makes it possible to predict in the shortest time the performance of a photovoltaic station in the context of specific days, months and along the year for any area and the method of solar panels arranging. One of powerful tools of computer mathematics, which recently has been widely used to simulate various technical systems, is the MATLAB environment. Due to the built-in Simulink package, MATLAB allows to realize visual-oriented block simulation of dynamic systems, in particular, various devices of functional electronics, solar cells and photovoltaic modules. This paper proposes an approach to modeling the operation of real photovoltaic panels in the MATLAB/Simulink environment using information from the panel technical documentation as input data for the Solar Cell block. The method for determining the input parameters of the Solar Cell block, which are not specified in the technical documentation of the solar panel, is presented. This method is based on the analysis of the light current-voltage characteristic of the panel for standard test conditions in the approximation of a single-diode equivalent circuit of a photovoltaic cell. Proposed method was tested on the example of JAP6(BK) 60-250/3BB, AS-6P30-270W and LG290N1C panels from the world's leading manufacturers. A visual-oriented model based on Solar Cell block was created in the MATLAB/Simulink environment to build the light characteristics of photovoltaic panels. The light characteristics of the JAP6(BK) 60-250/3BB, AS-6P30-270W and LG290N1C panels were simulated using created model, and main parameters of the panels under standard test conditions were calculated. Comparison of the panels parameters determined from the simulated light characteristics with the data presented in the technical documentation showed that for all panels the deviation of the simulation results from the documentation parameters is less than permissible 3%. This indicates the acceptability of the proposed approach to modeling the operation of real photovoltaic panels in the MATLAB / Simulink environment.