Solar radiation modelling for the simulation of photovoltaic systems

The effect of 12 different combinations of diffuse–global correlations and tilted surface radiation models on the accuracy of PV output simulation of a grid-connected photovoltaic (PV) system was studied using statistical methods. A site specific diffuse–global correlation was developed using local insolation data and the performance of this model was compared with those of two other diffuse–global correlations. The impact of diffuse–global correlations on the calculated inclined insolation for four different tilted surface radiation models was investigated on annual, seasonal and monthly basis. The accuracies of predicted 45° inclined plane insolation and PV output were improved when the site specific diffuse–global correlation was used in the main simulation model. The error between measured and predicted inclined insolation was higher in winter than summer. The prediction of PV output was improved by using an isotropic sky tilted surface radiation model instead of the anisotropic models. The accuracy of PV output was also improved when the proposed diffuse–global correlation was used.     

Estimated monthly average global radiation for Turkey and its comparison with observations

Global solar radiation data obtained from actinographs of the Turkish State Meterological Service were compared with data obtained from pyrheliometers that were established recently to determine if the actinograph data were usable in practice. It has been found that the observed actinograph data have a rather high error rate with 14.7% annual and 42.1% monthly averages. It has, however, not been possible to smooth these errors as they are absolutely random. Thus, a well known quadratic model was used to produce available radiation data. A quadratic relationship between solar insolation and duration of solar radiation data has been investigated in order to estimate monthly average global irradiance for Ankara, Antalya, Samsun, Konya, Urfa and zmir. The data from August 1993 to July 1995 have been used in the quadratic model. But, observations of zmir differ from other stations covered for about four years. Solar insolation data used in the quadratic model were obtained from the pyrheliometer with model CR10. Duration of bright sunshine data were obtained from a Siap, Frans or Müller sunshine recorder with 60° global lens. A general quadratic formula was found that represents the whole of Turkey. The estimated monthly average global solar radiation data, then, were produced from this quadratic formula. Comparison of the estimated and measured values showed that the quadratic model was able to estimate global radiation with about a 4% annual relative error and the estimated data seemed to be more reliable than the data measured by actinograph.     

On the investigation of photovoltaic output power reduction due to dust accumulation and weather conditions

Certain environmental conditions such as accumulation of dust and change in weather conditions affect the amount of solar radiation received by photovoltaic (PV) panel surfaces and thus have a significant effect on panel efficiency. This study conducted an experimental investigation in Surabaya, Indonesia, on the effect of these factors on output PV power reduction from the surface of a PV module. The module was exposed to outside weather conditions and connected to a measurement system developed using a rule-based model to identify different environmental conditions. The rule-based model, a clear sky solar irradiance model that included solar position, and a PV temperature model were then used to estimate the PV output power, and tests were also conducted using an ARM Cortex-M4 microcontroller STM32F407 as a standalone digital controller equipped with voltage, current, temperature, and humidity sensors to measure real time PV output power. In this system, humidity was monitored to identify dusty, cloudy, and rainy conditions. Validated test results demonstrate that the prediction error of PV power output based on the model is 3.6% compared to field measurements under clean surface conditions. The effects of dust accumulation and weather conditions on PV panel power output were then analyzed after one to four weeks of exposure. Results revealed that two weeks of dust accumulation caused a PV power output reduction of 10.8% in an average relative humidity of 52.24%. Results of the experiment under rainy conditions revealed a decrease in PV output power of more than 40% in average relative humidity of 76.32%, and a decrease in output power during cloudy conditions of more than 45% in an average relative humidity of 60.45% was observed. This study reveals that local environmental conditions, i.e., dust, rain, and partial cloud, significantly reduce PV power output.     

Study on control method of the stand-alone direct-coupling photovoltaic – Water electrolyzer

Based on photovoltaic as an energy resource and hydrogen as an energy carrier, we propose control methods of a photovoltaic-water electrolyzer (PV-WE) system that efficiently generates hydrogen by controlling the number of WE cells. The advantage of this direct coupling between PV and WE is that the power loss due to the DC/DC converter is avoided. Here, several different methods to control the number of WE cells in a stand-alone PV-WE system that is isolated from the electrical grid according to the weather condition were investigated: IV estimation method, PV module temperature method and PV current method. In the IV estimation method, first the current–voltage (IV) characteristics of the PV are calculated based on the measured solar insolation and PV module temperature, and then the nonlinear equation of the IV characteristics of the PV is solved to determine the optimum number of WE cells. In the PV module temperature method, only the PV module temperature and PV output current are used for control. In the PV current method, only the PV output current is used, and thus this method is the easiest among these methods to actually implement. Neither the PV module temperature method nor the PV current method requires solving any nonlinear equation of the IV characteristics of the PV. Each of these three control methods experimentally achieved a high maximum power point tracking efficiency (MPPT efficiency = actual PV output/maximum PV output), which was 98% or higher. Furthermore, to compare each control method, simulations of the PV-WE system were carried out using measured insulation and PV module temperature data for a 1-year period. Each of these three control methods also achieved a high MPPT efficiency in the annual simulations.     

Improving Photovoltaic Module Efficiency Using Water Cooling

An effective way of improving efficiency and reducing the rate of thermal degradation of a photovoltaic (PV) module is by reducing the operating temperature of its surface. This can be achieved by cooling the module and reducing the heat stored inside the PV cells during operation. In this paper, long-term performance modeling of a proposed solar-water pumping system is carried out. The system, which is used for irrigation purposes, consists of a PV module cooled by water, a submersible water pump, and a water storage tank. Cooling of the PV panel is achieved by introducing water trickling configuration on the upper surface of the panel. An experimental rig is developed to investigate and evaluate PV module performance with the proposed cooling technique. The experimental results indicated that due to the heat loss by convection between water and the PV panel's upper surface, an increase of about 15% in system output is achieved at peak radiation conditions. Long-term performance of the system is estimated by integrating test results in a commercial transient simulation package using site radiation and ambient temperature data. The simulation results of the system's annual performance indicated that an increase of 5% in delivered energy from the PV module can be achieved during dry and warm seasons.     

The impact of array inclination and orientation on the performance of a grid-connected photovoltaic system

The impact of PV surface orientation and inclination on grid-connected photovoltaic system performance under maritime climates was investigated using validated TRNSYS simulations. Insolation, PV output, PV efficiency, inverter efficiency, system efficiency, performance ratio (PR) and PV savings were estimated annually, seasonally and on monthly bases for various surface inclinations and orientations. Incident insolation and PV output were maximum for a surface with inclination 30° facing due south and minimum for a vertical surface with orientation 90° east or west from south. The monthly optimum collection angle maximising incident insolation varied from 10° to 70°. For the particular location and system studied, the maximum annual PV efficiency, the inverter efficiency, the PR and the system efficiency were for a south-facing surface with an inclination of 20°. For a horizontal surface, the monthly variation of system parameters was significant over a year. For time-dependent tariff rates, the annual PV savings were higher for a system oriented with same orientation towards the west than east from south while for constants tariff rates, the PV savings was the same for east or west orientation from south.     

Predicting total solar irradiation values using artificial neural networks

This study explores the possibility of developing an artificial neural networks model that could be used to predict monthly average daily total solar irradiation on a horizontal surface for locations in Uganda based on geographical and meteorological data: latitude, longitude, altitude, sunshine duration, relative humidity and maximum temperature. Results have shown good agreement between the predicted and measured values of total solar irradiation. A correlation coefficient of 0.997 was obtained with mean bias error of 0.018 MJ/m² and root mean square error of 0.131 MJ/m². Overall, the artificial neural networks model predicted with an accuracy of 0.1% of the mean absolute percentage error.     

Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks

This paper presents an application of the neural networks for identification of the maximum power (MP) and the normal operating power (NOP) of a photovoltaic (PV) module. Two neural networks are developed; the first is the maximum power neural network (MPNN) and the second is the normal operating power neural network (NOPNN). The two neural networks receive the solar radiation and the PV module surface temperature as inputs, and estimate the MP and the NOP of a PV module as outputs. The training process for the two neural networks used a series of input/output data pairs. The training inputs are the solar radiation and the PV module surface temperature, while the outputs are the PV module MP for the MPNN and the PV module NOP for the NOPNN. The results showed that, the proposed neural networks introduced a good accurate prediction for the PV module MP and NOP compared with the measured values.     

Neural network based estimation of maximum power generation from PVmodule using environmental information

This paper presents an application of an artificial neural network for the estimation of maximum power generation from PV module. The output power from a PV module depends on environmental factors such as irradiation and cell temperature. For the operation planning of power systems, the prediction of the power generation is inevitable for PV systems. For this purpose, irradiation, temperature and wind velocity are utilized as the input information to the proposed neural network. The output is the predicted maximum power generation under the condition given by those environmental factors. The efficiency of the proposed estimation scheme is evaluated by using actual data on daily, monthly and yearly bases. The proposed method gives highly accurate predictions compared with predictions using the conventional multiple regression model     

The determination of hourly insolation on an inclined plane using a diffuse irradiance model based on hourly measured global horizontal insolation

Using only measured hourly values of global insolation on a horizontal surface, a method has been developed for computing the corresponding hourly values of insolation on a surface inclined at any angle and oriented in any direction. The method uses a solar radiation model in which the diffuse component is calculated from global horizontal radiation using three different relationships; the appropriate equation is selected according to the value of the ratio of measured hourly global insolation to hourly global insolation computed for clear sky conditions. The method has been checked using measured hourly values in Melbourne over a 5-yr period of insolation on both a horizontal surface and a plane inclined at 38° to the horizontal facing north. The differences between the computed hourly values and the measured hourly values are found to be approximately normally distributed about zero with a standard deviation of 0.16 MJ m⁻². This method is particularly useful for predicting the heat output of inclined solar flat plate collectors when only measured global horizontal insolation is available, which is often the case. Good agreement was found between the predicted output of a typical collector using measured 38° insolation and the computed hourly values using this method. Since the method has been checked only against Melbourne data it should be applied elsewhere with caution, but it is believed to have general application.     

Unveiling the solar resource potential for photovoltaic applications in Mauritius

Mauritius is considered to have high solar resource potential but it has not yet been fully quantified and exploited due to the lack of valid solar energy data. This paper unveils the solar potential of Mauritius. Ground-based measurements were performed at intervals of 30 s in order to obtain accurate global horizontal irradiance data which can depict all changes in solar power. The latter is used to evaluate average monthly global horizontal irradiance, maximum irradiance, monthly average insolation and monthly sky clearness index. A solar geometry model was used to define the average monthly, seasonal and yearly maximum elevations and extraterrestrial radiation. Measurement data were compared to Meteonorm and NASA SSE 3-hourly averaged solar data. Comparison shows that average irradiance values are in good agreement, whereas insolation and sky clearness values obtained from external sources are inferior to high quality measurement data. The results, presented in this paper, complement solar data of Meteonorm and NASA SSE and secondly, provides PV and solar engineers as well as scientists with highly valuable information on the solar resource of Mauritius that can be used during planning and design of PV systems as well as for conducting further research in Mauritius and surrounding regions.     

Empirical relations for the determination of solar radiation in Ibadan, Nigeria

Variations in the average monthly, seasonal and daily patterns of total radiation, relative humidity, temperature and sunshine duration in Ibadan have been presented. Various empirical models relating solar radiation to the basic climatological parameters such as relative humidity, sunshine hours, temperature as well as geographical declination, latitude and altitude factors were investigated from the viewpoint of obtaining appropriate empirical formulae to determine solar radiation from such common parameters. The average monthly total radiation is shown to be predicted with reasonable accuracy by a couple of empirical formulae with the best result obtained from a newly proposed formula. The new formula is shown to give also good agreement when applied to the analysis of annual daily insolation data on a seasonal basis. Finally, the proposed formula predicts a linear relationship between the daily diffuse radiation and the daily total radiation in line with the Liu and Jordan model but with the fiffuse component generally higher in qualitative agreement with observations in the tropics.     

Investigation of cloudless solar radiation with PV module employing Matlab-Simulink

This paper focuses on a novel approach to the prediction of Voltage-Current (V-I) characteristics of a Photovoltaic panel under varying weather conditions and also the modelling of hourly cloudless solar radiation to provide the insolation on a PV module of any orientation, located at any site. The empirical model developed in this study uses standard specifications together with the actual solar radiation and cell temperature. This proposed work develops a Matlab-Simulink model to generate solar radiation at any location and for any time of the year. A new model for V-I characteristics and maximum power operation of a Photovoltaic (PV) module is also presented, which aims to model the effect on V-I and P-V curves of varying climatic conditions. Moreover, this model has been implemented using the Matlab-Simulink and is used to investigate the effect of meteorological conditions on the performance of a PV module generator. Thus the combined model of cloudless solar radiation and the photovoltaic module provides a tool that may be loaded in the library for analysis purpose. It is found that the predicted solar radiation strongly agrees with the experimental data.     

A simplified model for generating sequences of global solar radiation data for isolated sites: Using artificial neural network and a library of Markov transition matrices approach

The purpose of this work is to develop a hybrid model which will be used to predict the daily global solar radiation data by combining between an artificial neural network (ANN) and a library of Markov transition matrices (MTM) approach. Developed model can generate a sequence of global solar radiation data using a minimum of input data (latitude, longitude and altitude), especially in isolated sites. A data base of daily global solar radiation data has been collected from 60 meteorological stations in Algeria during 1991–2000. Also a typical meteorological year (TMY) has been built from this database. Firstly, a neural network block has been trained based on 60 known monthly solar radiation data from the TMY. In this way, the network was trained to accept and even handle a number of unusual cases. The neural network can generate the monthly solar radiation data. Secondly, these data have been divided by corresponding extraterrestrial value in order to obtain the monthly clearness index values. Based on these monthly clearness indexes and using a library of MTM block we can generate the sequences of daily clearness indexes. Known data were subsequently used to investigate the accuracy of the prediction. Furthermore, the unknown validation data set produced very accurate prediction; with an RMSE error not exceeding 8% between the measured and predicted data. A correlation coefficient ranging from 90% and 92% have been obtained; also this model has been compared to the traditional models AR, ARMA, Markov chain, MTM and measured data. Results obtained indicate that the proposed model can successfully be used for the estimation of the daily solar radiation data for any locations in Algeria by using as input the altitude, the longitude, and the latitude. Also, the model can be generalized for any location in the world. An application of sizing PV systems in isolated sites has been applied in order to confirm the validity of this model.     

Study of diffuse and global radiation characteristics in India

Recently, the Indian Meteorological Department has made available, for 13 locations in India, pyranometric data for total and diffuse radiation on an hourly and daily basis. The period of observation is from 1957 to 1975. This data is analysed to reexamine the correlations between monthly-average daily values of diffuse and total insolation and between hourly and daily insolation. The relationship between monthly diffuse/total and total/extraterrestrial ratios is found to be linear. The present correlation implies that the diffuse component is significantly larger than that predicted by other correlations. No noticeable effect of location or seasonal variation was found. Comparison of the present data with earlier studies for India indicates a trend of increasing diffuse radiation with the lapse of time. The ratio of hourly to daily insolation agrees with the Liu and Jordan correlation for total insolation but differs significantly for diffuse radiation. The need for refinement of the Liu and Jordan correlation between hourly and daily diffuse radiation is pointed out.     

Solar ponds—Corrections

Variations in the average monthly, seasonal and daily patterns of total radiation, relative humidity, temperature and sunshine duration in Ibadan have been presented. Various empirical models relating solar radiation to the basic climatological parameters such as relative humidity, sunshine hours, temperature as well as geographical declination, latitude and altitude factors were investigated from the viewpoint of obtaining appropriate empirical formulae to determine solar radiation from such common parameters. The average monthly total radiation is shown to be predicted with reasonable accuracy by a couple of empirical formulae with the best result obtained from a newly proposed formula. The new formula is shown to give also good agreement when applied to the analysis of annual daily insolation data on a seasonal basis. Finally, the proposed formula predicts a linear relationship between the daily diffuse radiation and the daily total radiation in line with the Liu and Jordan model but with the fiffuse component generally higher in qualitative agreement with observations in the tropics.     

Simplified method for predicting photovoltaic array output

A simplified procedure for predicting the long term, monthly average electrical output of photovoltaic arrays is presented. It is restricted to passively cooled, max-power tracked arrays, but is applicable to both south facing, fixed flat arrays and 2-D tracked concentrators. The procedure combines basic parameters characterizing the array with the local monthly mean temperature and the monthly K_T (ratio of the total radiation on the horizontal to the extraterrestrial radiation) to yield a monthly average array efficiency which, when multiplied by the monthly array insolation gives the electrical energy output.     

Validation of the Sandia model with indoor and outdoor measurements for semi-transparent amorphous silicon PV modules

This paper presents a set of indoor and outdoor measurement methods and procedures to determine the empirical coefficients of the Sandia Array Performance Model (SAPM) for a semi-transparent amorphous silicon (a-Si) PV module. After determining and inputting the total 39 parameters into the SAPM, the dynamic power output of the a-Si PV module was predicted. In order to validate the accuracy of using SAPM for simulating the energy output of the a-Si PV module, a long-term outdoor testing campaign was conducted. The results indicated that the SAPM with indoor and outdoor measured coefficients could accurately simulate the energy output of the a-Si PV module on sunny days, but it didn't work well on overcast days due to the inappropriate spectral correction as well as the equipment measuring error caused by the intense fluctuation of solar irradiance on overcast days. Specifically, all the errors between the simulated daily energy output and the measured one were less than 4% on sunny days. In order to achieve a better prediction performance for a-Si PV technologies, the SAPM was suggested to incorporate a more comprehensive spectral correction function to correct the impact of solar spectrum on overcast days in future.     

Improved artificial neural network method for predicting photovoltaic output performance

To ensure the safety and stability of power grids with photovoltaic (PV) generation integration, it is necessary to predict the output performance of PV modules under varying operating conditions. In this paper, an improved artificial neural network (ANN) method is proposed to predict the electrical characteristics of a PV module by combining several neural networks under different environmental conditions. To study the dependence of the output performance on the solar irradiance and temperature, the proposed neural network model is composed of four neural networks, it called multi- neural network (MANN). Each neural network consists of three layers, in which the input is solar radiation, and the module temperature and output are five physical parameters of the single diode model. The experimental data were divided into four groups and used for training the neural networks. The electrical properties of PV modules, including I–V curves, P– V curves, and normalized root mean square error, were obtained and discussed. The effectiveness and accuracy of this method is verified by the experimental data for different types of PV modules. Compared with the traditional single-ANN (SANN) method, the proposed method shows better accuracy under different operating conditions.     

Statistical comparison of correlations for estimation of the diffuse fraction of global radiation

We compare the statistical performance of eight correlations for estimating the diffuse fraction of the monthly daily global horizontal radiation with predicted monthly values of diffuse insolation for 40 locations in 15 countries with varied meteorological conditions and geographical locations. A new useful correlation is presented based on measured diffuse insolation records for selected stations. The correlations of Collares-Pereira and Rabl, Hay, and Page yield good estimates.