Cell Temperature Model Research Articles

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.

Photovoltaics literature survey (no. 182)

Photovoltaics literature survey (no. 182)

Optimizing the orientation of solar photovoltaic systems considering the effects of irradiation and cell temperature models with dust accumulation

To cope with the growing installation capacities of solar photovoltaic (PV) systems in desert areas, it is necessary to revisit the energy production models and the optimal angles of PV panels given the significant impacts of ambient temperature, wind speed, dust accumulation, and cleaning frequency. In this study, these four factors are examined for four PV technologies (polycrystalline, microcrystalline, monocrystalline, and thin-film) at three cities in Jordan, Egypt, and Tunisia using precise ground-level meteo-solar measurements. Different models are compared to estimate the diffuse irradiance, as well as account for the effects of operating temperature, wind speed, and dust accumulation on energy production and optimal tilt and azimuth angles of the panels. The results reveal 1.5 % higher energy production estimates using the isotropic model, compared to the anisotropic model in the summer months. Considering the cooling effect of wind speed decreases the operating cell temperature drops by up to 7.05 % for thin film panels. The annually produced energy decreases by 24 % when the panels are cleaned bi-monthly. When the dust accumulation rate doubles, the energy production decreases by ∼10 % for all studied cases. Also, the variations in optimal tilt and azimuth angles with dust accumulation rate are within ∼3.0°.

Optimal sizing of a solar water pumping system for Koyli Alpha Village, Senegal

Our objective is to solve problems of water supply in the village of Koyli Alpha, in Senegal. Theirs boreholes are supplied by diesel fuels causing environmental drawbacks and the populations don’t satisfy their water demand. In order to bring a positive response, we used solar energy to give back the borehole’s autonomous and proposed intuitive and numerical methods applying on solar water pumping for finding the best method. A previous study used intuitive methods for determining the size of various components. In order to optimize the energy production, we propose two numerical sizing approaches in order to have an optimal operation. Then, we developed two solar cell temperature models in the numerical sizing method and did a simulation of system operating in MATLAB software. The first model of solar cell temperature depends only on the ambient temperature and the second one combines wind speed and ambient temperature. The results of simulation showed that among these numerical sizing methods, we choose the second solar cell temperature expression, which gives the best performance. The numerical sizing method which uses the second solar cell temperature model yields to the reduction of battery’s size and the total life cycle cost found in the intuitive method, by 54% and 32%, respectively.

A general radial basis function neural network assisted hybrid modeling method for photovoltaic cell operating temperature prediction

Accurate and reliable prediction of photovoltaic (PV) cell operating temperature is vital for performing accurate output power prediction. Although numerous mathematical models have been developed to capture the effect of environmental variables on PV cell temperature, the prediction accuracy needs to be further improved and a relatively general modeling framework needs to be developed to enhance adaptability for different PV cell types. In this study, a novel radial basis function (RBF) neural network assisted hybrid modeling strategy is proposed to predict the PV cell temperature. The hybrid model is designed as an explicit mathematical formulation combined with an RBF neural network assisted correcting factor. The known function formulation is induced from prior knowledge and the unknown correcting factor is modeled by the RBF neural network. The hybrid cell temperature model is combined with the equivalent circuit model and the effectiveness of cell temperature and output power prediction is evaluated. The results illustrate that the proposed method can perform accurately cell temperature and output power prediction for both laboratory and commercial plants. It is thus indicated that the proposed hybrid modeling strategy could provide a potential general solution framework of cell temperature and output power prediction for different PV cells.

Validation of Thermal Models for Polycrystalline Photovoltaic Module Under Derna City Climate Conditions

The main objective of the present paper is to compare nine different cell temperature models available in the literature with data measured under real Derna city climatic conditions (a semi arid climate) for month of August. The study focuses on a comparison of nine theoretical models to calculate the cell temperature based on the experimental measurements such as the ambient temperature, irradiance, and wind speed in some of the models. The presently used models are explicit, depending on the easily measurable parameters and of wide applicability. Six statistical quantitative indicators are used to evaluate the cell temperature models analysed, namely, R2, RMSE, RRMSE, MAE, MBE and MARE. The cell temperature correlations presently studied, first order linear models depending on the ambient temperature, solar irradiation incident on the panel and voltage output, provide the most accurate cell temperature estimations at Derna city climatic conditions.

A new regression model to predict BIPV cell temperature for various climates using a high-resolution CFD microclimate model

ABSTRACT Understanding of cell temperature of Building Integrated Photovoltaics (BIPV) is essential in the calculation of their conversion efficiency, durability and installation costs. Current PV cell temperature models mainly fail to provide accurate predictions in complex arrangement of BIPVs under various climatic conditions. To address this limitation, this paper proposes a new regression model for prediction of the BIPV cell temperature in various climates and design conditions, including the effects of relative PV position to the roof edge, solar radiation intensity, wind speed, and wind direction. To represent the large number of possible climatic and design scenarios, the advanced technique of Latin Hypercube Sampling was firstly utilized to reduce the number of investigated scenarios from 13,338 to 374. Then, a high-resolution validated full-scale 3-dimensional Computational Fluid Dynamics (CFD) microclimate model was developed for modelling of BIPV’s cell temperature, and then was applied to model all the reduced scenarios. A nonlinear multivariable regression model was afterward fit to this population of 374 sets of CFD simulations. Eventually, the developed regression model was evaluated with new sets of unused climatic and design data when a high agreement with a mean discrepancy of 3% between the predicted and simulated BIPV cell temperatures was observed.

Validation of Thrmal Models for Polycrystalline Photovoltaic Module Under Derna City Climate Conditions

The main objective of the present paper is to compare nine diffrent cell temperature models available in the literature with data measured under real Derna city climatic conditions (a semi arid climate) for month of August. Th study focuses on a comparison of nine theoretical models to calculate the cell temperature based on the experimental measurements such as the ambient temperature, irradiance, and wind speed in some of the models. Th presently used models are explicit, depending on the easily measurable parameters and of wide applicability. Six statistical quantitative indicators are used to evaluate the cell temperature models analysed, namely, R2, RMSE, RRMSE, MAE, MBE and MARE. The cell temperature correlations presently studied, fist order linear models depending on the ambient temperature, solar irradiation incident on the panel and voltage output, provide the most accurate cell temperature estimations at Derna city climatic conditions

Identification of Solar Cell Temperature Model Using Differential Evolution Algorithm

Establishing a general and precise solar cell temperature model is of crucial importance for photovoltaic system modeling and the loss analysis of output power and conversion efficiency. Based on the complex mechanism of solar cell temperature, this paper studies the steady state estimation model of solar cell temperature. Firstly, based on the approximate linear relationship of air temperature, solar radiation intensity, wind speed and solar cell temperature, the polynomial model of solar cell temperature is established and the unknown parameters of the model are extracted with the differential evolution algorithm. Secondly, Experimental acquisition platform is built to reduce the effects of air humidity solar incidence angle on the PV cell temperature. Finally, it shows that the estimated steady-state model is reliable and it fits better performance than other literature submitted through the experimental comparison.

Solar cell temperature prediction model of support vector machine optimized by particle swarm optimization algorithm

Establishing a general and precise solar cell temperature model is of crucial importance for photovoltaic system modeling, the loss analysis of output power, and conversion efficiency. According to the complex mechanism of solar cell temperature, in this paper we study the steady state thermal model (SSTM) of solar cell temperature and accurate prediction model of method of support vector machine (SVM). Firstly, based on the approximate linear relationship among air temperature, solar radiation intensity, wind speed and solar cell temperature, the polynomial model of solar cell temperature is established and the unknown parameters of the model are extracted with the improved differential evolution algorithm. Secondly, in order to improve the accuracy of SVM prediction model, the particle swarm optimization algorithm is adopted to optimize the parameters (including kernel parameter g and penalty factor C from the radial basis function kernel) of SVM. After the input/output sample set is determined and the training set and test set are classified, a prediction model of solar cell temperature based on particle swarm optimization support vector machine is established. Finally, experimental acquisition platform is built to reduce the influences of air humidity, solar incidence angle, and thermal hysteresis effects on PV cell temperature. Through contrasting experiments, it is shown that the established fitting of the SSTM is better than the models given in other literature, and the prediction model is reliable, comprehensive and simple. The selected parameter optimization algorithm is superior to genetic algorithm and cross-validation method established on the optimization performance, and the accuracy of prediction model is superior to the prediction performance of back propagation neural network and identified SSTM.

Real-time analytical model for predicting the cell temperature modules of photovoltaic water pumping systems

In this paper an energy balance model to predict the cell temperature modules of photovoltaic water pumping system was developed. Results from this model are experimentally validated and also compared to results from published cell temperature models. The models were used to predict real-time performance from a PV water pumping systems in the desert of Medenine, south of Tunisia using 60-min intervals of measured performance data during one complete year. Statistical analysis of the predicted results and measured data highlight possible sources of errors and the limitations and/or adequacy of existing models, to describe the temperature and efficiency of PV-cells and consequently, the accuracy of performance of PV water pumping systems prediction models.

Real-time performance models for photovoltaic water pumping systems

This purpose of this paper is to develop and validate a model to accurately predict the cell temperature of a photovoltaic (PV) module that adapts to various mounting configurations, mounting locations, and climates while only requiring readily available data from the module manufacturer. Results from this model are also compared with results from published cell temperature models. The models were used to predict real-time performance from a PV water pumping systems in the desert of Medenine, south of Tunisia using 60-min intervals of measured performance data during one complete year. Statistical analysis of the predicted results and measured data highlights possible sources of errors and the limitations and/or adequacy of existing models, to describe the temperature and efficiency of PV-cells and consequently, the accuracy of performance of PV water pumping systems’ prediction models.