Model Of Photovoltaic Panel Research Articles

Hygroscopic hydrogel-based cooling system for photovoltaic panels: An experimental and numerical study

Thermal management is an essential aspect of photovoltaic (PV) system design because of the negative effects of high temperatures on the efficiency of PV panels. The use of hygroscopic hydrogels for the evaporative cooling of PV panels is an emerging technique that has attracted attention owing to the high latent heat and recyclability of these materials. However, a comprehensive understanding of the heat and mass transport mechanisms in hygroscopic hydrogels is necessary. Factors that would ensure the long-term performance of PV panel/hydrogel (PV/HG) systems under real-world conditions are still poorly understood, and the intrinsic properties of hydrogels and the effect thereof on their cooling performance have hitherto remained unexplored. Herein, we propose a mathematical model for hydrogel-cooled PV panels to study the heat and mass transfer mechanisms of PV/HG systems in practical environments. The model is used to examine the impact of the thermal conductivity, diffusion coefficient, and thickness of the hydrogel on the performance of the PV/HG system. Unlike previously reported approaches to enhance the efficiency, in this study, the external heat transfer resistance and internal mass transfer resistance were identified as the primary controlling factors. Enhancement of the thermal conductivity of the hydrogel is shown to have a limited effect on the performance, whereas improving the water diffusion within the hydrogel significantly boosts the system efficiency. Optimally, the thickness of the hydrogel must be such that it balances the heat and mass transfer distances with the available water volume. During the summer in Guangzhou, China, a 6-mm thick layer of hydrogel lowered the temperature of a PV cell by up to 7.5 °C to increase the daily power generation by 2.1%. Analysis of the nationwide situation showed that hydrogels have substantial potential for lowering the peak PV panel temperatures (by as much as 19.3 °C) to mitigate fluctuations in the output power. Our findings offer valuable insights into the design of PV/HG systems towards improving their performance.

PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network.

Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations are one of the popular research topics in PV panel technology. The PV panel parameters could be used for PV panel health monitoring and fault diagnosis. Recently, a PV panel parameters estimation method based in neural network and numerical current predictor methods has been developed. However, in order to further improve the estimation accuracies, a new approach of PV panel parameter estimation is proposed in this paper. The output current and voltage dynamic responses of a PV panel are measured, and the time series of the I-V vectors will be used as input to an artificial neural network (ANN)-based PV model parameter range classifier (MPRC). The MPRC is trained using an I-V dataset with large variations in PV model parameters. The results of MPRC are used to preset the initial particles' population for a particle swarm optimization (PSO) algorithm. The PSO algorithm is used to estimate the PV panel parameters and the results could be used for PV panel health monitoring and the derivation of maximum power point tracking (MMPT). Simulations results based on an experimental I-V dataset and an I-V dataset generated by simulation show that the proposed algorithms can achieve up to 3.5% accuracy and the speed of convergence was significantly improved as compared to a purely PSO approach.

Green energy as a key element of the implementation of the concept of distributed generation

Purpose. The purpose of this article is to study the role of "Green energy" in the concept of generation distribution, to analyze the contribution of renewable sources to the stability of energy. The main tasks include improving models of electric photo panels and wind generators to achieve environmental and economic efficiency. Methodology. Mathematical modeling of photovoltaic panels and wind generators, analysis of the effect of the precision factor and the efficiency factor of the inverter on power generation. A comparative analysis of the obtained results with real data is used to validate the models. The aerodynamic characteristics of the wind generator and the influence on the output power are studied. An optimized model for forecasting the efficiency of hybrid systems using green technologies is being developed. Findings. The obtained research results reveal the key contribution of "Green Energy" to the concept of distributed generation. The methods of mathematical model panels and the use of photovoltaic generators, together with the application of the current coefficient and taking into account the efficiency of the inverter, made it possible to increase the accuracy of forecasting electricity generation. The developed model takes into account the aerodynamic characteristics of the wind generator, emphasizing the realized possibility of exceeding the nominal power in accordance with the real characteristics. The obtained results are compared with real data for validation and confirmation of the effectiveness of the system. Originality. It consists in improving the model of photovoltaic panels, taking into account the influence of the inverter and the aerodynamic features of the wind generator. The work found that the largest generation of wind generators on earth is from October to February, indicating the advantage of combining solar and wind energy. Practical value. It consists in optimizing the forecasting of electricity generation in hybrid systems, which determined the increase in accuracy and adaptability to real conditions. Your study represents the potential for the development of "Green Energy" in communities with a large area and medium capacity.

Thermal Modeling of Photovoltaic Panel for Cell Temperature and Power Output Predictions under Outdoor Climatic Conditions of Jodhpur

The rise in the temperature severely affects photovoltaic cell efficiency and hence its power output. Moreover, it also causes the development of thermal stresses that may reduce their life span. Thus, there is a need for an accurate estimation of the cell’s working temperature. In this paper, a detailed thermal model based on various heat transfer modes involved and their governing equations has been presented to estimate the cell temperature of a PV module using MATLAB software under different climatic and solar insolation conditions. In order to validate the presented model, an experimental setup has been built and operated under actual outdoor conditions of Jodhpur, a city in the Thar Desert of Rajasthan. For the peak summer month of June, the predicted glass cover outer surface temperature has been found to be within 0.2–4.5°C of experimentally measured values and the back sheet temperature is found to be within 0.5–5.5°C. The predicted and measured power outputs have been found to be within 0.85–1.2 W while the efficiency values are within 0.17–0.38%. For the early summer month of April, the variations are 0.13–4.1°C, 0.2–4.1°C, 0.44–1.65 W, and 0.1–0.5% for glass cover temperature, back sheet temperature, power output, and efficiency, respectively. Thus, the predictions of the developed thermal model have exhibited a good agreement with the experimental results. The maximum glass cover temperatures recorded were 60°C and 65.5°C when the ambient temperatures were 35°C and 42°C near the noon for the early summer and peak summer day experiments, respectively. The presented model can be used to generate a year-round cell temperature data for the known environmental data of a location, which can help in the selection or development of appropriate cooling technology at the planning stage of the installation of a solar PV plant.

Characterization and Sensitivity Analysis of a Photovoltaic Panel

The characterization of photovoltaic (PV) panels is crucial both in post-manufacturing quality control and operational phases. To achieve this purpose, models of equivalent electronic circuits are utilized. The modeling of photovoltaic panels can be simulated using single-diode and double-diode models. These models differ in the number of parameters due to the inclusion of one or two diodes, resulting in non-linearity within the mathematical descriptions of these systems. In this study, a single-diode model with 5 parameters has been employed to analyze the system’s behavior. Sensitivity analysis and a comparative study of two different numerical methods were conducted to assess the estimation of the maximum power. Furthermore, the internal and external factors influencing the performance of photovoltaic panels were studied and analyzed. To simulate panel degradation, both the serial resistance and temperature were considered as slow time-varying internal and external parameters, affecting the I-V characteristic and the Maximum Power Point. This work presents a comparative study of heuristic search algorithms applied to the two models, accompanied by a simulation of a real-time application.

Superellipse model: An accurate and easy-to-fit empirical model for photovoltaic panels

The accurate representation of the photovoltaic (PV) characteristic curves especially at maximum power point (MPP) are essential for the real-time performance evaluation of PV panels. Over the years, equivalent circuit models which are based on the conversion behavior of PV panels have been the only approach employed in its modeling, simulation, and analysis. However, since the resulting I–V characteristic equation is inherently nonlinear and implicit, the full range enumeration of the PV characteristic curves is usually obtained in a complicated way. Thus, this paper proposes a novel empirical model for modeling and analysis of PV panels. Due to the unique similarities between the geometric shapes of a superellipse and the graphical characteristics of the I–V curve, an explicit mathematical correlation describing the behavior of PV panels can be established. By outlining a step-by-step procedure, the parameter extraction procedures are illustrated. As a result, the straightforward description of the full-range parameter fitting extraction of the I–V curve is greatly simplified. To validate the superiority of the proposed model over the conventional single-diode model, the model accuracy is evaluated according to the IEC EN 50530 standard. The simulation results revealed that irrespective of PV panel specification, cell material, and ambient conditions, the proposed empirical model maintains a low absolute current and power errors within the vicinity of MPP.

Daily prediction method of dust accumulation on photovoltaic (PV) panels using echo state network with delay output

Dust accumulation over time can be one of the main causes of uncertainty in the output of photovoltaic (PV) systems. In order to better understand these losses, this paper established a daily dust accumulation prediction model for PV panels based on the delay output echo state network (DESN). A pigeon-inspired optimization (PIO) algorithm with adaptive Cauchy (AC) mutation strategy was proposed, which can optimize the reservoir parameters (such as leakage rate, spectral radius, and input scaling) of DESN, shorten the solution time, and improve search speed. Based on typical meteorological and air quality data, as well as daily accumulated dust weight recorded from the experimental platform, model training and testing were carried out. According to the Pearson correlation coefficient, the relationship between the environment parameters (humidity, wind speed, wind direction, PM2.5, PM10, and rainfall) and the dust accumulation was obtained. The results show that the prediction accuracy of AC-PIO-DESN is better than other methods for meteorological and air quality data. The mean absolute percentage error (MAPE) for humidity, irradiance, PM2.5 and PM10 were 4.0743%, 4.4958%, 10.6231% and 12.8402%, respectively. In addition, the proposed daily dust prediction model has good robustness for 10-day samples, with an average relative error ranging from 0.65% to 54%. This method can provide data support for grid scheduling and PV panel cleaning strategy of PV power plants.

Active observer-based T-S fuzzy control scheme for maximum power point tracking in PV systems

This paper addresses the problem of maximum power point tracking of photovoltaic (PV) systems in the presence of model uncertainty as well as varying load and atmospheric conditions using techniques based on a Takagi–Sugeno fuzzy model. The proposed approach relies on the linear matrix inequality tool, Lambert W function, and the Newton–Raphson method. First, adopting a quadratic Lyapunov function, an active observer-based fuzzy non-parallel distributed compensation (non-PDC) controller is designed for asymptotic tracking of the desired reference input. Next, to subdue the impact of uncertainty on the PV system, the closed-loop nominal system is regarded as a reference model, and then the main control law is developed using an online lumped uncertainty estimator and keeping the nominal control law within the staple controller. This control law does not require that the bounds on uncertainties be known. The reference voltage is determined by a novel maximum power point-seeking algorithm that is organized based on the one-diode model of PV panel, Lambert W function, and Newton–Raphson method. Finally, simulations are performed for three scenarios to point out the merits and effectiveness of the proposed methodology in the presence of system uncertainties, environmental changes, and load variations.

PV Panel Model Parameter Estimation by Using Neural Network

Photovoltaic (PV) panels have been widely used as one of the solutions for green energy sources. Performance monitoring, fault diagnosis, and Control of Operation at Maximum Power Point (MPP) of PV panels became one of the popular research topics in the past. Model parameters could reflect the health conditions of a PV panel, and model parameter estimation can be applied to PV panel fault diagnosis. In this paper, we will propose a new algorithm for PV panel model parameters estimation by using a Neural Network (ANN) with a Numerical Current Prediction (NCP) layer. Output voltage and current signals (VI) after load perturbation are observed. An ANN is trained to estimate the PV panel model parameters, which is then fined tuned by the NCP to improve the accuracy to about 6%. During the testing stage, VI signals are input into the proposed ANN-NCP system. PV panel model parameters can then be estimated by the proposed algorithms, and the estimated model parameters can be then used for fault detection, health monitoring, and tracking operating points for MPP conditions.

Simple thermal-electrical model of photovoltaic panels with cooler-integrated sun tracker

This paper presents a simple thermal-electrical model of a photovoltaic panel with a cooler-integrated sun tracker. Based on the model and obtained weather data, we analyzed the improved overall efficiency in a year as well as the performance in each typical weather case for photovoltaic panels with fixed-tilt systems with a tilt angle equal to latitude, fixed-tilt systems with cooler, a single-axis sun tracker, and a cooler-integrated single-axis sun tracker. The results show that on a sunny summer day with few clouds, the performance of the photovoltaic panels with the proposed system improved and reached 32.76% compared with the fixed-tilt systems. On a sunny day with clouds in the wet, rainy season, because of the low air temperature and the high wind speed, the photovoltaic panel temperature was lower than the cooler’s initial set temperature; the performance of the photovoltaic panel with the proposed system improved by 12.55% compared with the fixed-tilt system. Simulation results show that, over one year, the overall efficiency of the proposed system markedly improved by 16.35, 13.03, and 3.68% compared with the photovoltaic panel with the fixed-tilt system, the cooler, and the single-axis sun tracker, respectively. The simulation results can serve as a premise for future experimental models.

A Step-by-Step Guideline for Modeling of Photovoltaic Panel by Using ISIS-Proteus Software

A Step-by-Step Guideline for Modeling of Photovoltaic Panel by Using ISIS-Proteus Software

Impedance Spectroscopy for Diagnosis of Photovoltaic Modules Under Outdoor Conditions

This work is aimed at detecting degradation phenomena on photovoltaic (PV) panels working under real outdoor conditions by using the impedance spectroscopy technique. The impedance is measured by leaving the PV panel in the real-operative status corresponding to the maximum power point without altering its power production. Since variations on the series resistance are associated with multiple degradation phenomena, this study seeks to estimate changes in this parameter using a dynamic model fitted to the experimental impedance measurements. An external resistor is added in series to the PV panel for emulating the degraded conditions. Hence, the PV panel impedance measurements are compared in nominal and degraded conditions. The results show that the dynamic model provides higher accuracy in comparison with the series resistance variation identified through the single-diode model, which is the most usual approach. Thus, it is demonstrated the feasibility of detecting degradation effects by using the dynamic model of PV panels working under outdoor conditions.

An efficient capuchin search algorithm for extracting the parameters of different PV cells/modules

Constructing an equivalent circuit for the photovoltaic (PV) generating unit converging the real operation is a difficult process because of unavailability of some parameters. Many approaches have been conducted in this field; however, they have some problems in computational time and are stuck in local optima. Therefore, this study proposes a simple, robust, and efficient methodology-incorporated capuchin search algorithm (CapSA) to construct the equivalent circuit of the PV generating unit via identifying its parameters. The CapSA is selected as it is simple and requires less computational time in addition to exploration/exploitation balance that avoids local optima. The process is formulated as an optimization problem, which aims at minimizing the root mean square error (RMSE) between measured and simulated currents. A single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM) of different PV cells and panels operating at either constant or variable weather conditions are constructed. A comparison to different programmed metaheuristic approaches is conducted. The best RMSE values obtained by the proposed CapSA are 2.27804E-04, 1.3808E-04, and 1.5182E-04 for SDM, DDM, and TDM of PVW 752 cell, respectively. For the KC200GT panel, the proposed approach achieved the best fitness values of 3.4440E-04, 1.5617E-03, and 6.6008E-03 at 25°C, 50°C, and 75°C, respectively. The obtained results confirmed the superiority and competence of the proposed CapSA in constructing a reliable equivalent circuit for the PV cell/panel.

High Accuracy Testing of MPPT Proteus Model Performance for Photovoltaic System

Abstract This study examines the performance of the MPPT Proteus model. The high performance of this model was proposed for a photovoltaic system and tested using the most commonly known MPPT techniques. The proposed Proteus model is a simulator of a maximum power point tracking system for a photovoltaic panel connected to the DC-DC converter with digital control. The Proteus software performs simulation and implementation of the photovoltaic panel model based on a one-diode model and a two-diode model with high accuracy. Both photovoltaic panel models were validated by experimental measurements. Simulation results for tracking voltage, tracking current and tracking power show that this model performs satisfactorily. The theoretical evaluation confirms the high performance of the MPPT Proteus model, which offers a high degree of control and planning.

Step-By-Step Guide to Model Photovoltaic Panels: An Up-To-Date Comparative Review Study

The presented study conducted a substantial literature review regarding the electrical modeling of photovoltaic panels. All the main models suggested in the literature to predict a photovoltaic panel's electrical behavior were reviewed, and diode-based equivalent electrical circuit models were selected for further investigations. The study performed a step-by-step investigation, comparison, and classification, followed by an in-depth and critical analysis of the art state. All the main diode-based models suggested in the literature were classified. The models' unknown parameters and the corresponding extraction methods were introduced and compared based on their accuracy, computing costs, and applicability. Reviewing the literature indicates that the extracted parameters under standard test conditions must be adjusted to actual environmental conditions to precisely predict a photovoltaic panel's behavior. By taking that into account, all the applied modifications in the literature were introduced and categorized. Finally, the reviewed documents were assessed critically to identify the research gaps and challenges. The analysis noted that although intensive research studies have been done, there are still many challenges to be faced. Thus, some directions for future research are proposed to cope with the remaining shortages. The presented study could be considered a step-by-step guide for anyone who wants to model the electrical behavior of photovoltaic panels under any environmental conditions.

Modeling of Photovoltaic Panel with Matlab@Simulink and Investigation of Behaviour in Outdoor Conditions

Temiz ve yenilenebilir enerji kaynaklarından biri olan güneş enerjisi sera gazı emisyonu oluşturmaması, sınırsız kaynak potansiyeli ve kaynak erişilebilirliği gibi olumlu etkileri nedeniyle sıklıkla tercih edilmektedir. Bu nedenle ülkemizde güneş enerjisi santrallerinden elektrik üretimi oldukça yaygındır. Güneş enerjisinden elektrik üretimi, fotovoltaik (FV) hücrelerin güneş ışınımını elektrik enerjisine dönüştürmesi ile gerçekleşmektedir. Bu çalışmada Matlab@Simulink yazılımında modellenen monokristal (c-Si) yapıdaki bir FV panelin laboratuvar ve dış ortam koşullarındaki davranışları karşılaştırılmıştır. Daha sonra Osmaniye Korkut Ata Üniversitesi Mühendislik Fakültesi çatısına kurulan sistem ile dış ortam koşullarında bulunan FV panelin performans sonuçları incelenmiştir. Elde edilen sonuçlara göre model ve deneysel sistemin birbirleriyle uyumlu olduğu gözlemlenmiştir.

Lightweight Hot-Spot Fault Detection Model of Photovoltaic Panels in UAV Remote-Sensing Image.

Photovoltaic panels exposed to harsh environments such as mountains and deserts (e.g., the Gobi desert) for a long time are prone to hot-spot failures, which can affect power generation efficiency and even cause fires. The existing hot-spot fault detection methods of photovoltaic panels cannot adequately complete the real-time detection task; hence, a detection model considering both detection accuracy and speed is proposed. In this paper, the feature extraction part of YOLOv5 is replaced by the more lightweight Focus structure and the basic unit of ShuffleNetv2, and then the original feature fusion method is simplified. Considering that there is no publicly available infrared photovoltaic panel image dataset, this paper generates an infrared photovoltaic image dataset through frame extraction processing and manual annotation of a publicly available video. Consequently, the number of parameters of the model was 3.71 M, mAP was 98.1%, and detection speed was 49 f/s. A comprehensive comparison of the accuracy, detection speed, and model parameters of each model showed that the indicators of the new model are superior to other detection models; thus, the new model is more suitable to be deployed on the UAV platform for real-time photovoltaic panel hot-spot fault detection.

A novel hybrid numerical with analytical approach for parameter extraction of photovoltaic modules

• A Novel hybrid approach analytical/Numerical technique is proposed and validated. • The minimum value of ARE is obtained using our model and equal to 1.14%. • The obtained RMSE is 0.0031 A , it’s considered as the smallest among the values found by other model. • Low computational time is carried using our Hybrid approach compared with the others models. • The parameter extraction method is superior in terms of accuracy and convergence. Building an accurate mathematical model of photovoltaic modules is an essential issue for providing reasonable analysis, control and optimization of photovoltaic energy systems. Therefore, this study provides a new accurate model of photovoltaic Panels based on single diode Model. In this case, the proposed model is the link between two models which are the ideal model and the resistance network. All parameters are estimated based on hybrid Analytical/Numerical approach: three parameters photocurrent, reverse saturation current and ideality factor are obtained using an Analytical approach based on the datasheet provided by the manufacturer under Standard Test Conditions. The series and shunt resistances are obtained by using a Numerical approach similar to the Villalva's method in order to achieve the purpose of modeling the resistance network part. Our model is tested with data from the manufacturer of three different technologies namely polycrystalline, Mono-crystalline silicon modules and thin-film based on Copper Indium Diselenide, and for more accurate performance evaluation we are introducing the Average Relative Error and the Root Mean Square Error. The simulated Current-Voltage and Power-Voltage curves are in accordance with experimental characteristics, and there is a strong agreement between the proposed model and the experimental characteristics. The computation time is 0.23 s lower than those obtained using others approach, and all obtained results under real environment conditions are also compared with different models and indicated that the proposed model outperforms the others approach such as villalva’s and kashif’s method.

Photovoltaic (PV) Model Evaluation with Maximum Power Point Tracking (MPPT)

The use of solar energy is widespread due to increase in its demand across the world. This is because this form of energy can supply steady, stable, regular and clean energy. Evaluation of PV (photovoltaic) model with maximum power point tracking (MPPT) using matlab/simulink cell model for estimating the I-V characteristic curves of photovoltaic panel with respect to changes on environmental parameters (temperature and irradiance) and cell parameters (parasitic resistance and ideality factor) were evaluated. The PV model equations were modelled and simulated using MATLAB/SIMULINK Approach. This paper confirmed the usefulness of MATLAB/SIMULINK in the development of MPPT (maximum power point tracking) algorithm. Using a Shockley diode equation, an accurate simulink PV panel model was developed. It was concluded that the MPPT is able to achieve maximum power output when there is variation in solar irradiance.

Yearly performance of the photovoltaic active cooling system using the thermoelectric generator

The PV panel absorbs solar irradiation flux on the surface. Part of the absorbed flux generates electricity, and a more significant amount converts into heat. Different methods are used to maintain photovoltaic at low temperatures. Heat is transferred in all heat transfer forms conduction, convection, and radiation. A photovoltaic panel model is developed in the current study that consists of an active cooling technique. Active cooling systems developed model uses domestic water as a thermoelectric generator's heat sink, and the photovoltaic temperature is a thermoelectric generator heat source. The proposed system depends on domestic water flow from the storage tank to the domestic building system at ambient temperature and under gravity flows and no extra power cost in the water flow process. The active cooling process keeps the PV panel at a steady temperature for almost 2 h and decreases the PV panel temperature in Winter, Spring, and Summer to 295K, 302K, and 311K, respectively, which is sufficient. The results also show the panel efficiency and electrical power generation enhancement by 4% and 20%, respectively, when the efficiency enhancement was steady for 6 h even under transient irradiation flux.