- Email: [email protected]
Temperature measurement of solar module in outdoor operating conditions using thermal imaging
Infrared Physics and Technology 92 (2018) 134–138
Contents lists available at ScienceDirect
Infrared Physics & Technology journal homepage: www.elsevier.com/locate/infrared
Regular article
Temperature measurement of solar module in outdoor operating conditions using thermal imaging
T
⁎
Irshad , Zainul Abdin Jaffery, Ahteshamul Haque Electrical Engineering Department, Jamia Millia Islamia University, New Delhi 110025, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Outdoor operating temperature (OOT) Photovoltaic module Thermal imaging Maximum power point
In this paper, a method to determine the operating temperature of photovoltaic module in outdoor conditions using thermal imaging is presented. Importance of temperature in PV module performance is well known at design and monitoring level. As manufacturer provide module specifications at STC (25 °C, 1 KW/m2) and the outdoor conditions are different, so in order to know the change in actual output of PV module and to track maximum power point with temperature in real time, it is important to determine the exact temperature of PV module in outdoor operating conditions. Existing techniques to determine the OOT have many drawbacks as discussed in paper. To overcome those drawbacks, a non-invasive and more accurate technique is suggested to measure module temperature in operating conditions. An experimental setup was established in outdoor and infrared images of PV module were captured using a Flir Infrared camera. These images were further processed quantitatively to calculate the temperature of module. To validate the results, actual field output data was compared to values calculated with the help of well-established relations available in literature at different temperatures calculated using existing techniques. Parameters calculated using thermal imaging method were most nearer to the actual field output data.
1. Introduction Photovoltaic(PV) module temperature is one of the key parameter which needs to be determined precisely in order to estimate the output of PV power system at design level. The output of a PV module decreases considerably with an increase in temperature. For instance, a value lesser by 5 °C may result in over prediction of 2.25 percent in expected output DC power, which may be a significant value for large solar systems [1]. Manufacturer rating of PV module is provided at STC (Standard Test Condition), 25 °C with irradiance of 1 KW/m2 [2]. However in outdoor environment conditions, module operates at a higher temperature. During summer, as ambient temperature increases, real output of module decreases as compared to the values mentioned by manufacturer rating [3]. For performance assessment, monitoring and, Outdoor Operating Temperature (OOT) of PV module need to be determined. The value of OOT is of more importance to fetch maximum power from PV module i.e. to operate module at maximum power point, operating temperature can be used to determine voltage at maximum power [4]. Module temperature may also be used to determine the degradation rate of a PV module [5,6]. Hence operating temperature of a PV module is an important parameter and needs to be
⁎
determined as accurate as possible. A survey of relevant literature provides dozens of correlations relating PV Cell temperature (Tc) to the ambient temperature (Tamb), solar irradiation and local wind speed [7]. Since all these parameters are highly variable with time, PV module output is extremely sensitive to these variables. Considering the thermal environment of a photovoltaic cell, semiconductor (p-n junction) temperature is of primary interest. This is a difficult task as PV cells cannot be directly probed in fielded modules. A commonly used method is to determine the junction temperature by measuring open circuit voltage across the solar module [8] as given by Eq. (1)
Voc =
KT ⎛ Jsc ln + 1⎞ q ⎝ Jo ⎠ ⎜
Available online 24 May 2018 1350-4495/ © 2018 Elsevier B.V. All rights reserved.
(1)
where Jsc is short circuit current density and T is the junction temperature. Advantage of this method is that it gives average junction temperature across the whole module but the issues with this method it is an open circuit method cannot be used in grid connected conditions in real time. Another method used is to rely on discrete locations temperature measurement of a solar panel by attaching a temperature measurement
Corresponding author. E-mail addresses: [email protected] (Irshad), zjaff[email protected] (Z.A. Jaffery), [email protected] (A. Haque).
https://doi.org/10.1016/j.infrared.2018.05.017 Received 6 March 2018; Received in revised form 18 May 2018; Accepted 18 May 2018
⎟
Infrared Physics and Technology 92 (2018) 134–138
Irshad et al.
probe (RTD Sensor) (as shown in Fig. 2) on the back surface of module before encapsulation [9]. Drawback of this method is that it does not give the average temperature of the module as the cells where temperature probes are located do not represent the overall temperature distribution pattern of panel. Another problem is that temperature given by this method may differ considerably from actual average temperature of PV module due to the encapsulation on PV cells. As an example, the relative temperature coefficient of power for crystalline silicon modules is typically −0.45%/°C; therefore, if your measured back-of-module temperature is 7 °C low, expected dc power output will be over-predicted by about 3.2%, which is a significant amount for large PV systems. Another established procedure to determine the PV cell operating temperature involves use of normal operating cell temperature (NOCT). This method is for open rack mounted modules with a sunlight angle of 45° and at fixed condition (Irradiance: 800 W/m2, Cell Temperature: 45 °C, Air Temperature: 20 °C, Wind Velocity: 1 m/s, Mounting: open back side). Since the value of irradiance and temperature changes almost at every instant in outdoor environment, hence using NOCT may not give accuracy in results [10,11]. In the present work, a non-invasive method using infrared (IR) images of PV module has been used to measure the operating temperature of the PV module. Color pattern of IR images has been used to model the temperature of whole panel. Although IR imaging has been already used for PV module condition monitoring and fault detection such as overheating [12–14], cracks and damage in a solar panel, but most of the work has been a qualitative application of infrared imaging. Active and passive thermography as performance assessment tool has been used Botsaris et al. [15]. In the proposed work, IR images have been analyzed quantitatively to determine the temperature of PV module in outdoor conditions. Information extracted from the thermal signature of PV module was utilized to calculate average temperature. Proposed method overcomes the drawbacks of existing methods.
Fig. 1. Infrared images of a solar module.
Sensor Solar Panel IR Camera
Recording and Processing Unit A Voltmeter
V
Fig. 2. Experimental setup.
detector using an IR Camera (as shown in Fig. 1). Intensity of emitted radiations depends on the temperature of object. More the temperature of object more will be the emitted radiation power as given by Eq. (2). Boltzmann law gives the thermal radiation power radiated by a body in terms of temperature as
(1) This method gives the averages temperature of PV module by taking in consideration each and every cell of PV panel, and hence more accurate as compared to discrete location temperature measurement method. (2) As the method gives the temperature of the PV module at the instant at which IR image was taken, so varying environmental parameters do not make an impact on calculations. (3) It is a non-invasive method and can be used in variable environment conditions as compared to other methods.
Pr = ∈σAT 4
(2)
An IR camera detects infrared radiations in the same way as done by visible camera in visible spectrum. Infrared image gives a signature of temperature distribution on the surface of object. Variation of color pattern on IR images varies with change in temperature. To calculate average temperature using IR images, color intensity of each pixel was converted into corresponding temperature. This conversion of pixel intensity to temperature was done with the help of an established relation through camera calibration. Proposed algorithm is divided into two parts.
Apart from the above advantages, thermal imaging temperature measurement method can be used for maximum power point tracking by determining the voltage and power with the help of temperature measured in real time operating condition [16,17]. In the present work, Voltage and power calculated at maximum power point using a wellestablished empirical relation at three different temperatures i.e. Temperature calculated with Infrared imaging, NOCT and Back of panel temperature measurement is compared with actual field output data. Rest of the paper is organized as follows. In Section 2, proposed algorithm is given. Implementation of experimental work, results is given in Section 3. In Section 4, comparison between proposed method and other methods and possible future improvement in the proposed techniques is discussed.
2.1. Calibration of IR camera i.e. to devise an equation to convert pixel intensity into corresponding temperature Step 1: Fifteen different IR images of a PV module were taken by focusing the IR camera at different points (P1, P2, P3,…P15). Some of the images are shown in Fig. 1. Step 2: Intensity (I1, I2, I3,…I15) corresponding to each point is determined by MATLAB Tool, and corresponding temperature (T1, T2, T3,…T15) as indicated by IR camera of each marked point were determined. (Temperature shown 54.0 °C at upper left corner of Fig. 4 is the temperature of point P1 and intensity level at point P1 is 197), similarly all 15 points temperature and intensity was taken. Step 3: Regression analysis of Intensity and Temperature values (taken in Step 2) was done in order to formulate an equation between Intensity and Temperature as in the form given below:
2. Proposed algorithm In the present work, a novel algorithm has been suggested to calculate the temperature of a PV module using Infrared images in outdoor environment conditions. Every object above absolute zero temperature emits thermal radiations. These radiations are emitted in electromagnetic spectrum in Infrared range and can be detected by an Infrared 135
Infrared Physics and Technology 92 (2018) 134–138
Irshad et al.
(3)
Ti = X ∗Ii + Y
Once a standard equation is formulated for a particular Camera, this equation can be applied to any image taken by that camera. With this equation temperature of each and every pixel on any test image can be calculated. 2.2. Calculation of average temperature from a test image Step 1: Resize the image after removing the extra background pixels. Step 2: Find total number of pixels on IR image and take it as ‘n’. Step 3: Find the intensity of each pixel with MATLAB tool and store it in a variable Ii(i=1ton). Step 4: Calculate Ti as
Ti = X ∗Ii + Y (where i = 1 to n)
Fig. 4. Intensity and temperature of Point P1 for camera calibration.
(4)
Step 5: Calculate Average temperature (T) as:
1 T= n
Table 1 Solar module characteristics at W/m2 ).
i=1
∑ Ti ( ∘C)
(5)
n
IR Camera needs to be calibrated only once and after that it can be used for any number of images to determine the temperature, provided that distance and angle between Camera and PV module remains same. 3. Implementation and results As present work is to determine the Outdoor Environment Temperature (OOT), an experimental setup (shown in Fig. 3) was established in outdoor conditions (Solar Insolation: 700 W/m2; Wind Speed: 1 m/s; Temperature 38 °C: As per Indian Meteorological Department).
STC(25 °C,
Variables
Value
1000
Pmpp
40 W
Voc Isc Vmpp
21.90 V 2.45 A 17.40 V
Impp
2.30 A
Kv Ki
−0.32%/°C 0.04%/°C
Table 2 Specifications of IR camera. Model
FLIR TG165
Range Emissivity Basic accuracy Resolution Response time Detector
−25 to 380 °C 0.1–0.99 ± 1.5% 176 * 220 Pixels 150 ms FPA
Four pt1000 sensors probes were attached at 4 discrete locations at the back of the solar panel and an average temperature of the four was calculated. A special precaution was taken while capturing the IR images. 20 IR images were taken in a time span of 2 min and in the same time span, remaining two tasks were also completed (i.e. measuring output values of voltage and current and measuring temperature from back probes using RTD). This precaution was taken in order to minimize the error due to variation in temperature due to variation in environmental conditions
Fig. 3. Setup in outdoor environment.
Experimental work was divided into three tasks. All the three tasks were performed concurrently as mentioned below: (1) Capturing Infrared Images of PV module in operating conditions
3.1. Camera calibration IR Camera manufactured by Flir Inc., Model TG165 was used to take images of a Polycrystalline silicon type solar PV Module (36 Cells) manufactured by VikramSolar (specifications of Camera and PV module given in Tables 1 and 2. After camera calibration, temperature from 20 different IR images was determined as described in algorithm.
Temperature and Intensity data was taken from 15 different images by focusing the camera at 15 different points on panel. Among 3 RGB components of color intensity, only ‘R’ component was taken as given in Jaffery & Dubey [18]. By doing a regression analysis between temperature and intensity values of 15 points marked on IR images, an equation was established between intensity level and temperature as given by Eq. (6) and graph in Fig. 5.
(2) Recording output voltage and current using a Voltmeter and Ammeter as shown in Fig. 2. (3) Measuring temperature by attaching RTD sensor probe at the back of PV module.
136
Infrared Physics and Technology 92 (2018) 134–138
Irshad et al.
Table 4 Actual field output of solar module in outdoor conditions.
Fig. 5. Regression analysis between intensity and temperature values.
Current
Voltage
Power
2.35 2.35 2.32 2.30 2.26 2.01 1.44 1.30 1.18 1.02 0.92 0.81 0.69 0.56 0.37 0.27 0.18 0.08
0.81 1.37 3.77 7.80 10.39 12.23 13.22 15.20 16.22 17.06 17.47 17.74 18.05 18.45 18.85 19.00 19.16 19.30
1.166 1.945 5.353 11.076 14.750 16.266 19.037 19.760 19.140 17.401 16.072 14.369 12.455 10.332 6.975 5.130 3.449 1.544
Bold values shows maximum power point.
3.3. Validation of results To validate the proposed method, actual field output of PV module in the form of current and voltage was recorded and VMPP and PMPP (MPP: Maximum power point) was determined from the recorded data (see Table 4). These values of VMPP and PMPP were compared with VMPP and PMPP calculated using empirical relation given in Moradi et al. [19] at temperature T = TIR and T = Ts and T = TNOCT. Empirical relation [10] is given by Eqs. (8) and (9).
Vmpp = K1 (Voc, n + Kv (T −Tn ))
(8)
Impp = K2 (Isc, n + KI (T −Tn ))
(9)
Fig. 6. (a) & (b) Original IR image and resized image.
(6)
Ti = 0.1411 Ii + 28.811
where Tn: 25 °C TIR: Temperature determined by Thermal Imaging Method Ts: Temperature determined by RTD sensor method TNOCT: Nominal Operating Cell Temperature (45 °C) Voc, n : Open circuit voltage at STC (25 °C, 1000 W/m2) Isc,n: Short circuit current at STC (25 °C, 1000 W/m2) Voc: Open circuit voltage in outdoor environment conditions (32 °C, 650 W/m2 ) Kv: Temperature coefficient of open circuit voltage K1: Ratio of voltage at maximum power to open circuit voltage K2: Ratio of current at maximum power to short circuit current
3.2. Calculation of temperature of PV module from IR image This task was completed with the help of Matlab Tool software. IR image was resized and extra pixels (due to background scene captured by camera) were romoved from the original image. In this paper, we have shown calculations for the test image given in Fig. 6(a) and 6(b). Size of Fig. 4(b) was found to 20,468 Pixels (119 * 172). For each corresponding pixel intensity, Value of T was calculated using Eq. (5) as shown below in Table 3.
TIR =
1 20, 468
i=1
∑
Ti ( ∘C) = 54.6 ∘C
The values of voltage (Vmpp) and Power (Pmpp) at maximum power point at T = TIR, T = Ts and T = TNOCT were calculated using the Eqs. (8) and (9) as given below: V 15.2 K1 = V oc = 19.3 = 0.788 (from Table 4)
(7)
20,468
mpp
Kv = −0.32%/∘C (from Table 1) Impp 1.30 K2 = I = 2.35 = 0.553(from Table 4) sc ∘ KI = 0.04%/ C (from Table 1) Calculating Vmpp and Pmpp at T = TIR = 54.6 °C (Eq. (7)) Vmpp=15.62 V; Impp = 1.371A; Pmpp = 21.39W Calculating Vmpp and Pmpp at T = TS = 38.6°C (Table 5) Vmpp=16.50 V; Impp=1.362 A;Pmpp = 22.47W Calculating Vmpp and Pmpp at T = TNOCT = 45.0 °C Vmpp=16.15 V; Impp = 1.365A; Pmpp = 22.04W To determine the temperature using RTD Sensor method, Four pt1000 sensor probes were attached to the back of the solar module at four different locations as shown in Fig. 2. Reading of four sensors is given in Table 5.
Table 3 Conversion of intensity to corresponding temperature. Pixel (Pi) (I = 1–20,468)
Intensity(Ii) (I = 1–20,468)
Temperature (°C) Ti = 0. 1411Ii + 28. 811
P1 P2 P3 … … … … P20467 P20468
196 185 191 … … … … 178 181
56.5 54.9 55.7 … … … … 53.9 54.3
137
Infrared Physics and Technology 92 (2018) 134–138
Irshad et al.
the importance of accurate temperature measurement in outdoor operating conditions. Proposed technique can be used by a manufacturer to provide performance data to a consumer. Improvement in the results obtained using proposed method can be done by considering the error which may occur due to reflections from upper glass coating of PV module in consideration. The emissivity values for glass and silicon material is different, so may also be a source of error, which can be a topic to further work upon. Another precaution which must be taken while taking thermal images of PV module is that the distance and angle between PV module and IR camera must be kept same for camera calibration and for measuring temperature.
Table 5 Sensor readings at four different locations. Sensor
Temperture value
S1 S2 S3 S4
38.4 37.2 42.5 36.4 TS = 38.6 °C average
Correlation in Eq. (10) used in Coscun et al. [7] was also used to calculate Cell temperature (Tc) in order to compare the results. Coscun et al. have used many correlations, but in the present work, most accurate correlation from Coscun et al. have been picked and used to calculate temperature as given below
Tc = 1.31Ta + 0.6511 + 0.021556GT −0.00001063GT2−1.65Vw
5. Conflict of interest The authors declared that there is no conflict of interest.
(10)
References
As per the ambient environment data: Ta = 38 °C, GT = 700W/m2 , Vw = 1m/s Calculaing Tc using equation by putting above values, Tc = 63.88 °C Calculating Vmpp and Pmpp at T = TC = 63.88 °C Vmpp=19.40 V; Impp = 1.3759A; Pmpp = 26.69W
[1] E. Skoplaki, J.A. Palyvos, On the temperature dependence of photovoltaic module electrical performance: a review of efficiency/power correlations, Sol. Energy 83 (2009) 614–624. [2] Manufacturer Data Specification Sheet, VikramSolar, Available from: < https:// www.vikramsolar.com/wpcontent/uploads/2016/02/DS-60-ELD-Prime-E-R05screen.pdf > . [3] D.L. King, W.E. Boyson, J.A. Kratochvil, Photovoltaic array performance model, Report SAND2004-3535, 2004. Available from: < http://prod.sandia.gov/techlib/ access-control.cgi/2004/043535.pdf > . [4] R.F. Coelho, F.M. Concer, D.C. Martins, A MPPT approach based on temperature measurements applied in PV systems, in: Proceedings of the IEEE International Conference on Sustainable Energy Technologies (ICSET '10), pp. 1–6, December 2010. [5] Nima E. Gorji, Thermal runaway in thin film PV: temperature profile modeling, IEEE Transactions on Device and Materials Reliability, vol. PP, issue 99, 2014. [6] Ababacar Ndiaye, et al., A novel method for investigating photovoltaic module degradation, Energy Proc. 36 (2013) 1222–1236. [7] Can Coskun, Ugurtan Toygar, Ozgur Sarpdag, Zuhal Oktay, Sensitivity analysis of implicit correlations for photovoltaic module temperature: a review, J. Clean. Prod. 164 (2017) 1474–1485. [8] Priyanka Singh, N.M. Ravindra, Temperature dependence of solar cell performance—an analysis, Sol. Energy Mater. Sol. Cells ED-11(1) (Jan. 1959) 34–39. [9] Stefan Krauter, Alexander Preiss, Comparison of module temperature measurement methods, 34th IEEE Photovoltaic Specialists Conference (PVSC), 2009, Philadelphia, USA. [10] M.W. Davis, A.H. Fanney, B.P. Dougherty, Prediction of building integrated photovoltaic cell temperatures, J. Sol. Energy Eng. 123 (3) (2001) 200–210. [11] M.C.A. Garcia, J.L. Balenzategui, Estimation of photovoltaic module yearly temperature and performance based on nominal operation cell temperature calculations, Renew. Energy 29 (12) (2004) 997–2010. [12] Cl. Buerhopa et al., Reliability of IR-imaging of PV-plants under operating conditions, Sol. Energy Mater. Sol. Cells 107 (Dec. 2012) 154–164. [13] Z.A. Jaffery, A.k. Dubey, Irshad, A. Haque, Scheme for predictive fault diagnosis in photo-voltaic modules using thermal imaging, Infrared Phys. & Tech. 83 (June 2017) 182–187. [14] Danio Harley, Aerial solar thermography and condition monitoring of photovoltaic systems, IEEE Photovoltaic Specialists Conference (PVSC), Austin, 2012, 10.1109/ PVSC.2012.6317686. [15] J.A. Tsanakas, P.N. Botsaris, Passive and active thermographic assessment as a tool for condition monitoring performance of photovoltaic modules, J. Sol. Energy Eng. SME 133 (2) (2011) 1012–1016. [16] L. Zaghba et al., Intelligent control MPPT technique for PV module at varying atmospheric conditions using MATLAB/SIMULINK, International Renewable and Sustainable Energy Conference (IRSEC), Morocco, 2014. [17] Ahteshamul Haque, Maximum power point tracking (MPPT) scheme for solar photovoltaic system, Energy Technol. Pol. 1 (2014) 115–122. [18] Z.A. Jaffery, A.K. Dubey, Design of early fault detection technique for electrical assets using infrared thermography, Electr. Power Energy Syst. 63 (2014) 753–759. [19] M.H. Moradi, A.R. Reisi, A hybrid maximum power point tracking method for photovoltaic systems, Sol. Energy 85 (2011) 2965–2976.
4. Discussion The output power generated from a solar PV system largely depends on temperature of module which in turn depends on ambient environment conditions. It is important to study the behaviour of PV module under these conditions. We need to focus on non-invasive methods to measure the performance parameters because using noninvasive techniques panel can be remotely monitored. Table 6 Comparison between recorded field output and calculated output using empirical relation at different tempertures.
Recorded field output (from Table 4) at T = TNOCT at T = TS at T = TC at T = TIR
Vmpp (V)
Pmpp (W)
15.20
19.76
16.15 16.50 19.40 15.62
22.04 22.47 26.69 21.39
% diff in Vmpp
% diff in Pmpp
6.25% 8.55% 27.63% 2.76%
11.53% 13.71% 35.07% 8.24%
Bold values shows most accurate values among the methods taken for comparison in Table 6.
While having a look at Table 6, It is concluded that thermal imaging appears to be potentially more accurate method to determine the PV module temperature in outdoor operating environment. Percentage difference in voltage and power at maximum power point (2.76% and 8.24% respectively) is minimum at temperature measured by IR imaging method. All other temperature measurements methods give more error. Comparing output parameters as specified by manufacturer and actual output in outdoor conditions, it is inferred that actual output is far lower than specified output at standard test conditions, which shows
138
Contents lists available at ScienceDirect
Infrared Physics & Technology journal homepage: www.elsevier.com/locate/infrared
Regular article
Temperature measurement of solar module in outdoor operating conditions using thermal imaging
T
⁎
Irshad , Zainul Abdin Jaffery, Ahteshamul Haque Electrical Engineering Department, Jamia Millia Islamia University, New Delhi 110025, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Outdoor operating temperature (OOT) Photovoltaic module Thermal imaging Maximum power point
In this paper, a method to determine the operating temperature of photovoltaic module in outdoor conditions using thermal imaging is presented. Importance of temperature in PV module performance is well known at design and monitoring level. As manufacturer provide module specifications at STC (25 °C, 1 KW/m2) and the outdoor conditions are different, so in order to know the change in actual output of PV module and to track maximum power point with temperature in real time, it is important to determine the exact temperature of PV module in outdoor operating conditions. Existing techniques to determine the OOT have many drawbacks as discussed in paper. To overcome those drawbacks, a non-invasive and more accurate technique is suggested to measure module temperature in operating conditions. An experimental setup was established in outdoor and infrared images of PV module were captured using a Flir Infrared camera. These images were further processed quantitatively to calculate the temperature of module. To validate the results, actual field output data was compared to values calculated with the help of well-established relations available in literature at different temperatures calculated using existing techniques. Parameters calculated using thermal imaging method were most nearer to the actual field output data.
1. Introduction Photovoltaic(PV) module temperature is one of the key parameter which needs to be determined precisely in order to estimate the output of PV power system at design level. The output of a PV module decreases considerably with an increase in temperature. For instance, a value lesser by 5 °C may result in over prediction of 2.25 percent in expected output DC power, which may be a significant value for large solar systems [1]. Manufacturer rating of PV module is provided at STC (Standard Test Condition), 25 °C with irradiance of 1 KW/m2 [2]. However in outdoor environment conditions, module operates at a higher temperature. During summer, as ambient temperature increases, real output of module decreases as compared to the values mentioned by manufacturer rating [3]. For performance assessment, monitoring and, Outdoor Operating Temperature (OOT) of PV module need to be determined. The value of OOT is of more importance to fetch maximum power from PV module i.e. to operate module at maximum power point, operating temperature can be used to determine voltage at maximum power [4]. Module temperature may also be used to determine the degradation rate of a PV module [5,6]. Hence operating temperature of a PV module is an important parameter and needs to be
⁎
determined as accurate as possible. A survey of relevant literature provides dozens of correlations relating PV Cell temperature (Tc) to the ambient temperature (Tamb), solar irradiation and local wind speed [7]. Since all these parameters are highly variable with time, PV module output is extremely sensitive to these variables. Considering the thermal environment of a photovoltaic cell, semiconductor (p-n junction) temperature is of primary interest. This is a difficult task as PV cells cannot be directly probed in fielded modules. A commonly used method is to determine the junction temperature by measuring open circuit voltage across the solar module [8] as given by Eq. (1)
Voc =
KT ⎛ Jsc ln + 1⎞ q ⎝ Jo ⎠ ⎜
Available online 24 May 2018 1350-4495/ © 2018 Elsevier B.V. All rights reserved.
(1)
where Jsc is short circuit current density and T is the junction temperature. Advantage of this method is that it gives average junction temperature across the whole module but the issues with this method it is an open circuit method cannot be used in grid connected conditions in real time. Another method used is to rely on discrete locations temperature measurement of a solar panel by attaching a temperature measurement
Corresponding author. E-mail addresses: [email protected] (Irshad), zjaff[email protected] (Z.A. Jaffery), [email protected] (A. Haque).
https://doi.org/10.1016/j.infrared.2018.05.017 Received 6 March 2018; Received in revised form 18 May 2018; Accepted 18 May 2018
⎟
Infrared Physics and Technology 92 (2018) 134–138
Irshad et al.
probe (RTD Sensor) (as shown in Fig. 2) on the back surface of module before encapsulation [9]. Drawback of this method is that it does not give the average temperature of the module as the cells where temperature probes are located do not represent the overall temperature distribution pattern of panel. Another problem is that temperature given by this method may differ considerably from actual average temperature of PV module due to the encapsulation on PV cells. As an example, the relative temperature coefficient of power for crystalline silicon modules is typically −0.45%/°C; therefore, if your measured back-of-module temperature is 7 °C low, expected dc power output will be over-predicted by about 3.2%, which is a significant amount for large PV systems. Another established procedure to determine the PV cell operating temperature involves use of normal operating cell temperature (NOCT). This method is for open rack mounted modules with a sunlight angle of 45° and at fixed condition (Irradiance: 800 W/m2, Cell Temperature: 45 °C, Air Temperature: 20 °C, Wind Velocity: 1 m/s, Mounting: open back side). Since the value of irradiance and temperature changes almost at every instant in outdoor environment, hence using NOCT may not give accuracy in results [10,11]. In the present work, a non-invasive method using infrared (IR) images of PV module has been used to measure the operating temperature of the PV module. Color pattern of IR images has been used to model the temperature of whole panel. Although IR imaging has been already used for PV module condition monitoring and fault detection such as overheating [12–14], cracks and damage in a solar panel, but most of the work has been a qualitative application of infrared imaging. Active and passive thermography as performance assessment tool has been used Botsaris et al. [15]. In the proposed work, IR images have been analyzed quantitatively to determine the temperature of PV module in outdoor conditions. Information extracted from the thermal signature of PV module was utilized to calculate average temperature. Proposed method overcomes the drawbacks of existing methods.
Fig. 1. Infrared images of a solar module.
Sensor Solar Panel IR Camera
Recording and Processing Unit A Voltmeter
V
Fig. 2. Experimental setup.
detector using an IR Camera (as shown in Fig. 1). Intensity of emitted radiations depends on the temperature of object. More the temperature of object more will be the emitted radiation power as given by Eq. (2). Boltzmann law gives the thermal radiation power radiated by a body in terms of temperature as
(1) This method gives the averages temperature of PV module by taking in consideration each and every cell of PV panel, and hence more accurate as compared to discrete location temperature measurement method. (2) As the method gives the temperature of the PV module at the instant at which IR image was taken, so varying environmental parameters do not make an impact on calculations. (3) It is a non-invasive method and can be used in variable environment conditions as compared to other methods.
Pr = ∈σAT 4
(2)
An IR camera detects infrared radiations in the same way as done by visible camera in visible spectrum. Infrared image gives a signature of temperature distribution on the surface of object. Variation of color pattern on IR images varies with change in temperature. To calculate average temperature using IR images, color intensity of each pixel was converted into corresponding temperature. This conversion of pixel intensity to temperature was done with the help of an established relation through camera calibration. Proposed algorithm is divided into two parts.
Apart from the above advantages, thermal imaging temperature measurement method can be used for maximum power point tracking by determining the voltage and power with the help of temperature measured in real time operating condition [16,17]. In the present work, Voltage and power calculated at maximum power point using a wellestablished empirical relation at three different temperatures i.e. Temperature calculated with Infrared imaging, NOCT and Back of panel temperature measurement is compared with actual field output data. Rest of the paper is organized as follows. In Section 2, proposed algorithm is given. Implementation of experimental work, results is given in Section 3. In Section 4, comparison between proposed method and other methods and possible future improvement in the proposed techniques is discussed.
2.1. Calibration of IR camera i.e. to devise an equation to convert pixel intensity into corresponding temperature Step 1: Fifteen different IR images of a PV module were taken by focusing the IR camera at different points (P1, P2, P3,…P15). Some of the images are shown in Fig. 1. Step 2: Intensity (I1, I2, I3,…I15) corresponding to each point is determined by MATLAB Tool, and corresponding temperature (T1, T2, T3,…T15) as indicated by IR camera of each marked point were determined. (Temperature shown 54.0 °C at upper left corner of Fig. 4 is the temperature of point P1 and intensity level at point P1 is 197), similarly all 15 points temperature and intensity was taken. Step 3: Regression analysis of Intensity and Temperature values (taken in Step 2) was done in order to formulate an equation between Intensity and Temperature as in the form given below:
2. Proposed algorithm In the present work, a novel algorithm has been suggested to calculate the temperature of a PV module using Infrared images in outdoor environment conditions. Every object above absolute zero temperature emits thermal radiations. These radiations are emitted in electromagnetic spectrum in Infrared range and can be detected by an Infrared 135
Infrared Physics and Technology 92 (2018) 134–138
Irshad et al.
(3)
Ti = X ∗Ii + Y
Once a standard equation is formulated for a particular Camera, this equation can be applied to any image taken by that camera. With this equation temperature of each and every pixel on any test image can be calculated. 2.2. Calculation of average temperature from a test image Step 1: Resize the image after removing the extra background pixels. Step 2: Find total number of pixels on IR image and take it as ‘n’. Step 3: Find the intensity of each pixel with MATLAB tool and store it in a variable Ii(i=1ton). Step 4: Calculate Ti as
Ti = X ∗Ii + Y (where i = 1 to n)
Fig. 4. Intensity and temperature of Point P1 for camera calibration.
(4)
Step 5: Calculate Average temperature (T) as:
1 T= n
Table 1 Solar module characteristics at W/m2 ).
i=1
∑ Ti ( ∘C)
(5)
n
IR Camera needs to be calibrated only once and after that it can be used for any number of images to determine the temperature, provided that distance and angle between Camera and PV module remains same. 3. Implementation and results As present work is to determine the Outdoor Environment Temperature (OOT), an experimental setup (shown in Fig. 3) was established in outdoor conditions (Solar Insolation: 700 W/m2; Wind Speed: 1 m/s; Temperature 38 °C: As per Indian Meteorological Department).
STC(25 °C,
Variables
Value
1000
Pmpp
40 W
Voc Isc Vmpp
21.90 V 2.45 A 17.40 V
Impp
2.30 A
Kv Ki
−0.32%/°C 0.04%/°C
Table 2 Specifications of IR camera. Model
FLIR TG165
Range Emissivity Basic accuracy Resolution Response time Detector
−25 to 380 °C 0.1–0.99 ± 1.5% 176 * 220 Pixels 150 ms FPA
Four pt1000 sensors probes were attached at 4 discrete locations at the back of the solar panel and an average temperature of the four was calculated. A special precaution was taken while capturing the IR images. 20 IR images were taken in a time span of 2 min and in the same time span, remaining two tasks were also completed (i.e. measuring output values of voltage and current and measuring temperature from back probes using RTD). This precaution was taken in order to minimize the error due to variation in temperature due to variation in environmental conditions
Fig. 3. Setup in outdoor environment.
Experimental work was divided into three tasks. All the three tasks were performed concurrently as mentioned below: (1) Capturing Infrared Images of PV module in operating conditions
3.1. Camera calibration IR Camera manufactured by Flir Inc., Model TG165 was used to take images of a Polycrystalline silicon type solar PV Module (36 Cells) manufactured by VikramSolar (specifications of Camera and PV module given in Tables 1 and 2. After camera calibration, temperature from 20 different IR images was determined as described in algorithm.
Temperature and Intensity data was taken from 15 different images by focusing the camera at 15 different points on panel. Among 3 RGB components of color intensity, only ‘R’ component was taken as given in Jaffery & Dubey [18]. By doing a regression analysis between temperature and intensity values of 15 points marked on IR images, an equation was established between intensity level and temperature as given by Eq. (6) and graph in Fig. 5.
(2) Recording output voltage and current using a Voltmeter and Ammeter as shown in Fig. 2. (3) Measuring temperature by attaching RTD sensor probe at the back of PV module.
136
Infrared Physics and Technology 92 (2018) 134–138
Irshad et al.
Table 4 Actual field output of solar module in outdoor conditions.
Fig. 5. Regression analysis between intensity and temperature values.
Current
Voltage
Power
2.35 2.35 2.32 2.30 2.26 2.01 1.44 1.30 1.18 1.02 0.92 0.81 0.69 0.56 0.37 0.27 0.18 0.08
0.81 1.37 3.77 7.80 10.39 12.23 13.22 15.20 16.22 17.06 17.47 17.74 18.05 18.45 18.85 19.00 19.16 19.30
1.166 1.945 5.353 11.076 14.750 16.266 19.037 19.760 19.140 17.401 16.072 14.369 12.455 10.332 6.975 5.130 3.449 1.544
Bold values shows maximum power point.
3.3. Validation of results To validate the proposed method, actual field output of PV module in the form of current and voltage was recorded and VMPP and PMPP (MPP: Maximum power point) was determined from the recorded data (see Table 4). These values of VMPP and PMPP were compared with VMPP and PMPP calculated using empirical relation given in Moradi et al. [19] at temperature T = TIR and T = Ts and T = TNOCT. Empirical relation [10] is given by Eqs. (8) and (9).
Vmpp = K1 (Voc, n + Kv (T −Tn ))
(8)
Impp = K2 (Isc, n + KI (T −Tn ))
(9)
Fig. 6. (a) & (b) Original IR image and resized image.
(6)
Ti = 0.1411 Ii + 28.811
where Tn: 25 °C TIR: Temperature determined by Thermal Imaging Method Ts: Temperature determined by RTD sensor method TNOCT: Nominal Operating Cell Temperature (45 °C) Voc, n : Open circuit voltage at STC (25 °C, 1000 W/m2) Isc,n: Short circuit current at STC (25 °C, 1000 W/m2) Voc: Open circuit voltage in outdoor environment conditions (32 °C, 650 W/m2 ) Kv: Temperature coefficient of open circuit voltage K1: Ratio of voltage at maximum power to open circuit voltage K2: Ratio of current at maximum power to short circuit current
3.2. Calculation of temperature of PV module from IR image This task was completed with the help of Matlab Tool software. IR image was resized and extra pixels (due to background scene captured by camera) were romoved from the original image. In this paper, we have shown calculations for the test image given in Fig. 6(a) and 6(b). Size of Fig. 4(b) was found to 20,468 Pixels (119 * 172). For each corresponding pixel intensity, Value of T was calculated using Eq. (5) as shown below in Table 3.
TIR =
1 20, 468
i=1
∑
Ti ( ∘C) = 54.6 ∘C
The values of voltage (Vmpp) and Power (Pmpp) at maximum power point at T = TIR, T = Ts and T = TNOCT were calculated using the Eqs. (8) and (9) as given below: V 15.2 K1 = V oc = 19.3 = 0.788 (from Table 4)
(7)
20,468
mpp
Kv = −0.32%/∘C (from Table 1) Impp 1.30 K2 = I = 2.35 = 0.553(from Table 4) sc ∘ KI = 0.04%/ C (from Table 1) Calculating Vmpp and Pmpp at T = TIR = 54.6 °C (Eq. (7)) Vmpp=15.62 V; Impp = 1.371A; Pmpp = 21.39W Calculating Vmpp and Pmpp at T = TS = 38.6°C (Table 5) Vmpp=16.50 V; Impp=1.362 A;Pmpp = 22.47W Calculating Vmpp and Pmpp at T = TNOCT = 45.0 °C Vmpp=16.15 V; Impp = 1.365A; Pmpp = 22.04W To determine the temperature using RTD Sensor method, Four pt1000 sensor probes were attached to the back of the solar module at four different locations as shown in Fig. 2. Reading of four sensors is given in Table 5.
Table 3 Conversion of intensity to corresponding temperature. Pixel (Pi) (I = 1–20,468)
Intensity(Ii) (I = 1–20,468)
Temperature (°C) Ti = 0. 1411Ii + 28. 811
P1 P2 P3 … … … … P20467 P20468
196 185 191 … … … … 178 181
56.5 54.9 55.7 … … … … 53.9 54.3
137
Infrared Physics and Technology 92 (2018) 134–138
Irshad et al.
the importance of accurate temperature measurement in outdoor operating conditions. Proposed technique can be used by a manufacturer to provide performance data to a consumer. Improvement in the results obtained using proposed method can be done by considering the error which may occur due to reflections from upper glass coating of PV module in consideration. The emissivity values for glass and silicon material is different, so may also be a source of error, which can be a topic to further work upon. Another precaution which must be taken while taking thermal images of PV module is that the distance and angle between PV module and IR camera must be kept same for camera calibration and for measuring temperature.
Table 5 Sensor readings at four different locations. Sensor
Temperture value
S1 S2 S3 S4
38.4 37.2 42.5 36.4 TS = 38.6 °C average
Correlation in Eq. (10) used in Coscun et al. [7] was also used to calculate Cell temperature (Tc) in order to compare the results. Coscun et al. have used many correlations, but in the present work, most accurate correlation from Coscun et al. have been picked and used to calculate temperature as given below
Tc = 1.31Ta + 0.6511 + 0.021556GT −0.00001063GT2−1.65Vw
5. Conflict of interest The authors declared that there is no conflict of interest.
(10)
References
As per the ambient environment data: Ta = 38 °C, GT = 700W/m2 , Vw = 1m/s Calculaing Tc using equation by putting above values, Tc = 63.88 °C Calculating Vmpp and Pmpp at T = TC = 63.88 °C Vmpp=19.40 V; Impp = 1.3759A; Pmpp = 26.69W
[1] E. Skoplaki, J.A. Palyvos, On the temperature dependence of photovoltaic module electrical performance: a review of efficiency/power correlations, Sol. Energy 83 (2009) 614–624. [2] Manufacturer Data Specification Sheet, VikramSolar, Available from: < https:// www.vikramsolar.com/wpcontent/uploads/2016/02/DS-60-ELD-Prime-E-R05screen.pdf > . [3] D.L. King, W.E. Boyson, J.A. Kratochvil, Photovoltaic array performance model, Report SAND2004-3535, 2004. Available from: < http://prod.sandia.gov/techlib/ access-control.cgi/2004/043535.pdf > . [4] R.F. Coelho, F.M. Concer, D.C. Martins, A MPPT approach based on temperature measurements applied in PV systems, in: Proceedings of the IEEE International Conference on Sustainable Energy Technologies (ICSET '10), pp. 1–6, December 2010. [5] Nima E. Gorji, Thermal runaway in thin film PV: temperature profile modeling, IEEE Transactions on Device and Materials Reliability, vol. PP, issue 99, 2014. [6] Ababacar Ndiaye, et al., A novel method for investigating photovoltaic module degradation, Energy Proc. 36 (2013) 1222–1236. [7] Can Coskun, Ugurtan Toygar, Ozgur Sarpdag, Zuhal Oktay, Sensitivity analysis of implicit correlations for photovoltaic module temperature: a review, J. Clean. Prod. 164 (2017) 1474–1485. [8] Priyanka Singh, N.M. Ravindra, Temperature dependence of solar cell performance—an analysis, Sol. Energy Mater. Sol. Cells ED-11(1) (Jan. 1959) 34–39. [9] Stefan Krauter, Alexander Preiss, Comparison of module temperature measurement methods, 34th IEEE Photovoltaic Specialists Conference (PVSC), 2009, Philadelphia, USA. [10] M.W. Davis, A.H. Fanney, B.P. Dougherty, Prediction of building integrated photovoltaic cell temperatures, J. Sol. Energy Eng. 123 (3) (2001) 200–210. [11] M.C.A. Garcia, J.L. Balenzategui, Estimation of photovoltaic module yearly temperature and performance based on nominal operation cell temperature calculations, Renew. Energy 29 (12) (2004) 997–2010. [12] Cl. Buerhopa et al., Reliability of IR-imaging of PV-plants under operating conditions, Sol. Energy Mater. Sol. Cells 107 (Dec. 2012) 154–164. [13] Z.A. Jaffery, A.k. Dubey, Irshad, A. Haque, Scheme for predictive fault diagnosis in photo-voltaic modules using thermal imaging, Infrared Phys. & Tech. 83 (June 2017) 182–187. [14] Danio Harley, Aerial solar thermography and condition monitoring of photovoltaic systems, IEEE Photovoltaic Specialists Conference (PVSC), Austin, 2012, 10.1109/ PVSC.2012.6317686. [15] J.A. Tsanakas, P.N. Botsaris, Passive and active thermographic assessment as a tool for condition monitoring performance of photovoltaic modules, J. Sol. Energy Eng. SME 133 (2) (2011) 1012–1016. [16] L. Zaghba et al., Intelligent control MPPT technique for PV module at varying atmospheric conditions using MATLAB/SIMULINK, International Renewable and Sustainable Energy Conference (IRSEC), Morocco, 2014. [17] Ahteshamul Haque, Maximum power point tracking (MPPT) scheme for solar photovoltaic system, Energy Technol. Pol. 1 (2014) 115–122. [18] Z.A. Jaffery, A.K. Dubey, Design of early fault detection technique for electrical assets using infrared thermography, Electr. Power Energy Syst. 63 (2014) 753–759. [19] M.H. Moradi, A.R. Reisi, A hybrid maximum power point tracking method for photovoltaic systems, Sol. Energy 85 (2011) 2965–2976.
4. Discussion The output power generated from a solar PV system largely depends on temperature of module which in turn depends on ambient environment conditions. It is important to study the behaviour of PV module under these conditions. We need to focus on non-invasive methods to measure the performance parameters because using noninvasive techniques panel can be remotely monitored. Table 6 Comparison between recorded field output and calculated output using empirical relation at different tempertures.
Recorded field output (from Table 4) at T = TNOCT at T = TS at T = TC at T = TIR
Vmpp (V)
Pmpp (W)
15.20
19.76
16.15 16.50 19.40 15.62
22.04 22.47 26.69 21.39
% diff in Vmpp
% diff in Pmpp
6.25% 8.55% 27.63% 2.76%
11.53% 13.71% 35.07% 8.24%
Bold values shows most accurate values among the methods taken for comparison in Table 6.
While having a look at Table 6, It is concluded that thermal imaging appears to be potentially more accurate method to determine the PV module temperature in outdoor operating environment. Percentage difference in voltage and power at maximum power point (2.76% and 8.24% respectively) is minimum at temperature measured by IR imaging method. All other temperature measurements methods give more error. Comparing output parameters as specified by manufacturer and actual output in outdoor conditions, it is inferred that actual output is far lower than specified output at standard test conditions, which shows
138