Figure 1.
Electrical circuit of PV cell [21].
Citation: Jonathan R. Emerson, Jack A. Binks, Matthew W. Scott, Ryan P. W. Kenny, Daniel L. Eaves. Combined action observation and motor imagery therapy: a novel method for post-stroke motor rehabilitation[J]. AIMS Neuroscience, 2018, 5(4): 236-252. doi: 10.3934/Neuroscience.2018.4.236
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Nomenclatures: A: PV module area (m2); Eg: Energy gap of the semiconductor (eV); FF: Fill factor (%); ID, Io, Iph, Imp, IRS, Isc, Imo: Diode, saturation, photo-generated, maximum power point, reverse saturation, short circuit and module currents (A); k: Boltzmann constant 1.380649 × 10−23 (J K−1); n: Ideality factor; NP, NS: Number of cells in parallel and series; P: Electrical power (kW); q: Electronic charge = 1.602176634 × 10−19 (Coulombs); $ {R}_{S}, Rsh $: Series and Shunt resistance (Ohm); S: Solar irradiance (W/m2); $ {S}_{ref} $: Reference solar radiation (1000 W/m2); Tc: Cell temperature(℃); Tref: Reference temperature (25 ℃); V, VT, VMO, Vmp, Voc: Diode, thermal, module, maximum power point, open circuit voltages (V)
Greek Symbols: ƞ: Electrical efficiency(%); $ \alpha, \mathrm{\beta }, \delta, \mathrm{\gamma } $: Temperature coefficient of short circuit current, open circuit voltage, fill factor and maximum power (%/℃)
The photovoltaic phenomenon is the most important technology that has been used to benefit from sun energy. It was used to convert solar energy into electrical directly through the so-called solar cell [1]. As a result of the development that took place in this field, the researchers have begun to develop and improve the performance of different types of solar cells like multi-junction, perovskite, and quantum [2,3,4,5]. Generally, the output electrical characteristics of PV solar cells are directly affected by the temperature variation. Whereas, the efficiency of the PV cell/module decreased with increasing the temperature [6,7] by 0.5% for each 1 ℃ [8,9]. Therefore, the influence of temperature variation on the behavior of PV cell/module should be described by the indicator of temperature coefficient. Where, the module temperature positively effects on the short circuit current (Isc) and negatively on both open circuit voltage (Voc), maximum power (Pmax) and fill factor (FF) [10]. In indoor conditions, the method of measuring coefficient of temperature is considered inaccurate due to the difference between the characteristics of sun light spectrum and the light that used in simulator of test [11]. In the same manner, inaccuracy of outdoor test occurs because it is not possible to obtain a uniform change in the increasing of sunlight [12]. In open circuit, the temperature coefficient is assumed to be equal to 80–90% of temperature coefficient of maximum power [13] and can be calculated according to the assumption that the energy gap linearly change with the temperature range. While, in maximum power, the temperature coefficient is defined as the summation of temperature coefficient of the Isc, Voc, and FF [14]. The main characteristics of indoor and outdoor measurements can be summarized according to: (1) the PV cell in indoor test is illuminated with solar simulator, and (2) the temperature of the solar cell should be stable with ±2 ℃ and temperature range should be at least 30 ℃ [15,16]. Besides, the oscillating of irradiance in outdoor should be kept within ±2% from the total solar irradiance, the wind speed not exceed 2 m/s, and the PV module temperature should be kept with ±2 ℃ of ambient temperature. According to the literatures review, a number of researchers were presented numerical and experimental studies to analyse the influence of temperature on the PV module performance. The experimental studies were divided between indoor and outdoor measurements. For example, Indra [17] studied the influence of temperature on the output characteristics of the polycrystalline PV module. The outdoor tests show that the maximum power decreased by 17% with changing the PV module temperature from 15 to 60 ℃. Moreover, Amelia et al. [18] investigated the effect of operating temperature on the PV module mono-crystalline type. At first, the manufactured output characteristics of the PV was simulated by PVsyst software. Furthermore, outdoor experiments were investigated using I-V tracer (PROVA 200). In addition, the temperature distribution is determined using a digital thermal camera. The outdoor results showed that a variation of output characteristics of the PV module compared with STCs whereas the output power and the efficiency of the PV module decreases with the increasing of the PV working temperature. Different types of the PV modules were tested indoor and outdoor by [16] to measure the temperature coefficients of Isc and Voc. The results of indoor test showed that a good agreement of the temperature coefficient with the results of the outdoor results. Singh and Ravindra [19] studied the influence of the temperature between 25 to 250 ℃ on the output characteristics of various types of PV modules. Their study was proved that the increasing of temperature lead to decrease each of the Voc, FF and efficiency. According to Jehad Adeeb et al. [20] study, the thin film module has less affected by increasing the temperature with less temperature coefficient about 0.0984% as compared with the other types. Where, the temperature coefficients were recorded 0.109%, for Mono-crystalline and −0.124% for Poly-crystalline. Based on the above literature review, it can be conclude that there is a relationship between the PV temperature and the outputs of the PV module, and can be determine the effects of the temperature on the PV output by indoor and outdoor tests. However, it can be seen that there is a limitation of using numerical analysis to determine the effects of the temperature on the PV outputs. For that, the main objective of this work is to study and analyze the effect of temperature on the output electrical characteristics of polycrystalline PV module using a numerical model validated by experimental test. In the numerical model, the I-V and P-V curves of the PV module were presented under the effect of module temperature ranges from 25 to 65 ℃ and solar irradiance ranges from 200 to 1000 W/m2 using MATLAB software. In addition, the validation of the simulation results are performed with experimental results for the PV module under real weather conditions using I-V tracer (SEAWARD PV200).
The PV module is a group of solar cells connected in series or in parallel combination. Each cell has approximately 0.5 V. The electrical circuit of PV cell can be represented as shown in Figure 1.
From Shockley equation [22,23], the current and voltage of PV cell in zero illumination can be represented by:
| $ {I}_{D} = {I}_{0}\left({e}^{\left(\frac{V}{n{V}_{T}}\right)}-1\right) $ | (1) |
where: $ \mathrm{V} $ is the voltage across the cell, VT is the thermal voltage (k Tc/q), q is the electronic charge = 1.602176634 × 10−19 coulombs, $ {T}_{c} $ is the cell temperature (℃), k is the Boltzmann constant = 1.380649 × 10−23 J K−1 and n is the ideality factor of diode.
${I}_{0} $ is the diode saturation current (A) and can be defined as [24]:
| $ {I}_{0} = {I}_{RS}{\left(\frac{{T}_{c}}{{T}_{ref}}\right)}^{3}{e}^{\left(\frac{q{E}_{g}\left(\frac{1}{{T}_{ref}}-\frac{1}{{T}_{c}}\right)}{nk}\right)} $ | (2) |
where IRS is the reverse saturation current at STCs and Eg is the energy gap of the semiconductor used in the cell.
With the sunlight, the current of the solar cell is offset due to the photo current as [25,26]:
| $ I = {I}_{ph}-{I}_{0}\left({e}^{\left(\frac{qV}{nkT}\right)}-1\right) $ | (3) |
where: Iph is the photo-generated current (A) and expressed as [27]:
| $ {I}_{ph} = \frac{S}{{S}_{ref}}\left[{I}_{sc}+\alpha \left({T}_{c}-{T}_{ref}\right)\right] $ | (4) |
where: S is the instantaneous solar irradiance (kW/m2), $ {S}_{ref} $ is the reference solar irradiance (1 kW/m2), Isc is the short circuit current at STCs and $ \alpha $ is the temperature coefficient of the Isc.
In case of series resistance, the current generation described by [28]:
| $ I = {I}_{ph}-{I}_{0}\left({e}^{\left(\frac{q\left(V-I{R}_{s}\right)}{nkT}\right)}-1\right) $ | (5) |
where: $ {R}_{S} $ is the series resistance due to resistance of the silicon-bulk and contact material.
In case of parallel and series resistance, the current generation described by:
| $ I = {I}_{ph}-{I}_{0}\left({e}^{\left(\frac{q\left(V+I{R}_{S}~~~\right)}{nkT}~~~\right)}-1\right)-\frac{V+I{R}_{s}}{{R}_{sh}} $ | (6) |
where: Rsh is the shunt resistor due to inhomogeneity of the surface and loss-current at the solar cell edges.
The voltage of the solar cell can be written as:
| $ V = -I{R}_{S}+klog\left[\frac{{I}_{ph}-I+{I}_{0}}{{I}_{0}}\right] $ | (7) |
Equation 7 can be written in term of PV module as [24]:
In case of PV cells in series:
| $ {V}_{mo} = -{I}_{mo}{{N}_{S}R}_{S}+{N}_{S}klog\left[\frac{{I}_{ph}-{I}_{mo}+{I}_{O}}{{I}_{0}}\right] $ | (8) |
In case of PV cells in parallel:
| $ {V}_{mo} = -{I}_{mo}\frac{{R}_{S}}{{N}_{p}}+klog\left[\frac{{{N}_{S}I}_{ph}-{I}_{mo}+{N}_{p}{I}_{O}}{{N}_{p}{I}_{0}}\right] $ | (9) |
where: mo represents the abbreviation of module, NS and Np are the number of cells in series and parallel respectively.
Generally, production power of the PV module can be defined as [29]:
| $ \mathrm{P} = {\mathrm{N}}_{\mathrm{S}}\times {\mathrm{N}}_{\mathrm{p}}\times V\times I $ | (10) |
while, the efficiency can be defined as [30]:
| $ ƞ = \frac{P}{S\times A} $ | (11) |
where: A is a PV module area.
The fill factor represents the ratio of maximum power to the theoretical [30]:
| $ FF = \frac{{I}_{mp}{V}_{mp}}{{I}_{SC}{V}_{OC}} $ | (12) |
where $ {I}_{mp}~~{and~~V}_{mp} $ are the current and voltage at maximum power point.
The PV module efficiency depends on many types of losses. These losses can be confined as: thermalisation, transmission, emission, and angular mismatch and Carnot losses [10]. The increasing of PV temperature has a negative effect on these types of losses and can be represented by a temperature coefficients [31]. Commonly, the temperature coefficient is measured in indoor conditions and listed in the datasheet of the PV module. It can be classified into: Isc, $ {V}_{OC} $, FF and $ {P}_{max} $ coefficients.
The coefficient of temperature for Isc [10], Voc [32], FF [33,34] and for Pmax [14] can be represented by equations from 13 to 16 respectively.
| $ \alpha = \frac{{I}_{sc\left(Tc\right)-}~~~{I}_{sc\left(Tref\right)}}{{T}_{C}{-T}_{ref}} $ | (13) |
| $ \beta = \frac{{V}_{oc\left(Tc\right)-}~~~{V}_{oc\left(Tref\right)}}{{T}_{C}{-T}_{ref}} $ | (14) |
| $ \delta = \frac{1}{FF}\frac{dFF}{dT} $ | (15) |
| $ \gamma = \alpha +\beta +\delta $ | (16) |
A polycrystalline PV module made by Fortuner (FRS-165W) was selected in this study to achieve the experimental tests. The PV module is fixed on a steel frame with tilt angle 33o towards the south depending on the latitude of site as shown in Figure 2. The main specifications of the PV module is presented in Table 1. I-V tracer (SEAWARD PV200) was used to measure the output electrical characteristics of the PV module including ISc, VOC, Vmp, Imp, and Pmax. Furthermore, a solar meter (Survey 200R) devise was synchronized with the SEAWARD PV200 to measure the ambient conditions (air temperature and solar radiation) and back temperature of module. All the measured data were stored in a computer and reviewed by solar data logger and then displayed by SolarCert software. The wind speed was measured by a handle anemometer. In addition, an infrared thermal digital camera (visual IR thermometer VT02) was used to image of temperature distribution on the front and back sides of the PV module.
| Parameter | Value |
| Dimensions | 149 × 67 cm |
| Pmax | 165 W |
| Isc | 9.81 A |
| Voc | 22.05 V |
| Imp | 9.17 A |
| Vmp | 18 V |
| ƞ | 20 |
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Iraq is considered one of the hot and dry countries in the summer season. Where, the maximum temperature reaches almost more than 50 ℃, especially in August month. Four days (1/8/2021, 5/8/2021, 11/8/2021 and 13/8/2021(were selected to measure the parameters of the PV module. As shown in Figure 3, the maximum average ambient temperature was reached about 51 ℃ at noon between (1:00 pm and 2:00 pm). Besides, the average solar radiation was recorded a maximum value about 940 W/m2 at noon as shown in Figure 4. While, the average wind velocity was not exceed 0.5 m/s in the morning at 6:00 am as shown in Figure 5.
A MATLAB code was built based on the set of Eqs (1–16) and the technical data of PV module that tabulated in Table 1 to analyse the I-V and P-V curves of the PV module. In addition, the variation of VOC, Isc, Pmax and FF were represented with module temperature to analyze the temperature coefficients. The I-V and P-V curves were represented in ranges of module temperature and solar radiation between (25 to 65) ℃ and (200 to 1000) W/m2. While, the analysis of temperature coefficients were investigated at (600, 800, and 1, 000) W/m2. As shown in Figure 6, the Isc was varied from (9.6 to 9.8) A when the module temperature was changed from (25 to 65) ℃ at constant solar radiation 1000 W/m2. While, the Voc was decreased from (22.05 to 20.2) V resulting in a maximum power changed from (146 to 165) W as shown in Figure 7. It is observed that the increasing the temperature of PV module has critical effect on decreasing the voltage rather than increasing the current. This can be explained as follows: Increasing cell temperature lead to reduce of band gap energy, which indicates to increase the absorbed photons that results in producing more free charge carriers. In other words, lower energy needs to liberate the free charge carrier. This means that the number of flow electrons with more current and low energy will increase (drop in voltage) [30,35].
On the other side, at constant temperature (25 ℃) and solar radiation varied from (200 to 1000) W/m2, the Isc, VOC were varied from (2 to 9.8) A and from (20.1 to 22.05) V respectively resulting in a maximum power range from (31 to 165) W as shown in Figures 8 and 9.
The variation of output characteristics such as: Isc, Voc, Pmax, and FF with the module temperature are plotted in Figures (10–13) for three values of solar radiation (600, 800 and 1, 000) W/m2. As shown in figures, the relationship seems to be a linear model with increasing trend in Isc and decreasing trend in Voc, Pmax, and FF. The temperature coefficients were calculated from the slop of straight lines presented in Table 2. As shown, the temperature coefficient (α) of Isc was obviously changed from 0.058 to 0.091 %/℃ when the solar radiation increased from 600 to 1000 W/m2. While, the temperature coefficient (𝛽) of Voc, (𝜸) of Pmax and (δ) of FF were little changed. As presented in Table 2, the regression equations of module are characterized by a high coefficient of determination (R2) with a range from 0.962 to 0.999. This is an indication that this regression correlation can be used to predict the output electrical characteristics of the PV module.
| Solar radiation W/m2 | Linear models and temperature coefficients | |||
| Isc (A) | Voc (V) | FF (%) | P (W) | |
| 1000 | $\mathrm{I}_{\mathrm{sc}}=0.008 * T_{\mathrm{c}}+9.38\\ \alpha=0.0815 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.9992$ | $\mathrm{V}_{\mathrm{oc}}=-0.034 * \mathrm{~T}_{\mathrm{c}}+23\\ \beta=-0.15 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.994$ | $\mathrm{FF}=-0.120 * \mathrm{~T}_{\mathrm{c}}+80\\ \delta=-0.155 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.989$ | $\mathrm{P}=-0.43^* \mathrm{~T}_{\mathrm{c}}+180\\ \gamma=-0.26 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.993$ |
| 800 | $\mathrm{I}_{\mathrm{sc}}=0.0085^* \mathrm{~T}_{\mathrm{c}}+9.285\\ \alpha=0.086 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.9996$ | $\mathrm{~V}_{\mathrm{oc}}=-0.036 * \mathrm{~T}_{\mathrm{c}}+23\\ \beta=-0.163 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.987$ | $\mathrm{FF}=-0.12 * \mathrm{~T}_{\mathrm{c}}+79\\ \delta=-0.155 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.979$ | $\mathrm{P}=-0.43^* \mathrm{~T}_{\mathrm{c}}+170\\ \gamma=-0.26 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.997$ |
| 600 | $\mathrm{I}_{\mathrm{sc}}=0.0074 * \mathrm{~T}_{\mathrm{c}}+9217\\ \alpha=0.075 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.9993$ | $\mathrm{I}_{\mathrm{sc}}=-0.035 * \mathrm{~T}_{\mathrm{c}}+23\\ \beta=-0.158 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.986$ | $\mathrm{FF}=-0.11 * \mathrm{~T}_{\mathrm{c}}+78\\ \delta=-0.142 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.989$ | $\mathrm{P}=-0.38 * \mathrm{~T}_{\mathrm{c}}+160\\ \boldsymbol{\gamma}=-0.23 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.988$ |
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The experimental investigation was investigated during four days of August (01/08/2021), (05/08/2021), (11/08/2021) and (13/08/2021) from 6:00 am to 2:00 pm at the center of Middle Technical University-Baghdad, Iraq. The output characteristics of PV module such as: Isc, Imp, Voc, Vmp, Pmax, FF, and electrical efficiency (ƞele) in addition to its back temperature were measured as shown in Figure 14. From figure, the maximum value of PV temperature was recorded about 69 ℃ in 13/08/2021 at 1:00 pm while the minimum temperature was recorded about 37 ℃ in 01/08/2021 at 6:00 am. According to the average value, the PV module temperature was recorded about 37.5 ℃ and 63 ℃ as a minimum and maximum value respectively. The module temperature was varied due to the variation of solar radiation and ambient temperature.
Furthermore, the front and back temperatures of the PV module were imaged using thermal digital camera for one day 01/08/2021 to compare between them as shown in Figure 15. The images were taken every one hour between 8:00 am to 1:00 pm. Generally, the PV back temperature was recorded a higher than the front temperature. Where, the maximum temperature of front and back was recorded 58.9 ℃ and 64.9 ℃ respectively at 11:00 am. This can be explained by the fact that the PV module was installed at average height not exceeding 1m from the ground surface (i.e., it closer to the ground) therefore, the air currents were very low. In addition, more heat will reflect from the ground surface toward the back surface of module.
Figure 16 shows the variation of module voltage with time. As shown, the Voc was slightly decrease from 20.75 to 20.35 V while, the Vmp was sharply decrease from maximum value 18.55 to 15.9 V due to the higher increase of module temperature. In contrast to this, the Isc and Imp were increased. Where, the Isc was increased from 1.72 to 7.32 A while, Imp was increased from 1.4 to 6.9 A as shown in Figure 17.
As shown in Figure 4, the average solar irradiance was increased from 220 to 920 W/m2 during 6:00 am to 12:00 pm. Therefore, the electrical power was increased from 25.9 to 131.2 W then dropped to 109.7 W at 2:00 pm because of decreasing the solar irradiance to 770 W/m2 as shown in Figure 18. This is accompanied by a decline in the electrical efficiency (ƞele) of the module from 15.6% to 13.3% and decreasing in fill factor from 76% to 71%.
The temperature coefficients of current, voltage and maximum power were compared with same studies in literature [10,15,36] as presented in Table 3. The comparison was depended on the same type of PV modules (polycrystalline) that found in the previous studies. The structure of table was built according to technical data of PV module, ambient conditions and temperature coefficients. As presented in Table 3, the temperature coefficient of current (α) was close to the coefficient temperature in [36] at solar radiation 1000 W/m2 while, the coefficients of voltage (𝛽) and power max (𝜸) were recorded greater than those in [36]. In the same manner, all the temperature coefficients in present study recorded less than the coefficients of reference [10]. Otherwise, they recorded greater than the coefficients in reference [15]. On the other side, the linear model with Coefficient of Determination (R2) of the current, voltage and power equations were compared also with [36] at 1000 W/m2 as shown in Table 4. The comparison shows a good agreement between the two studies since the R2 value was very close.
| Ref. | Technical data | Country | Type of study | Solar radiationW/m2 | Temp. Module℃ | Temperature coefficients %/℃ | |||
| α (Isc) | β (Voc) | δ (FF) | 𝜸 (Pmax) | ||||||
| [36] | $\mathrm{V}_{\mathrm{oc}}=44.9 \mathrm{~V}\\ \mathrm{Isc}=5.75 \mathrm{~A}\\ \mathrm{~V}_{\mathrm{mp}}=36.2 \mathrm{~V}\\ \mathrm{I}_{\mathrm{mp}}=5.11 \mathrm{~A}\\ \mathrm{P}_{\max }=80 \mathrm{~W}$ | Thailand | Exp.Outdoor | 1000 | 40–65 | 0.08181 | −0.05185 | NA | −0.00038 |
| [10] | $V_\mathrm{o c}=0-50 \mathrm{~V}\\ I_{\mathrm{sc}}=0-15 \mathrm{~A}\\\mathrm{P}_{\max }>10 \mathrm{~W}$ | Norway | Exp.Outdoor | > 400 | 10–70 | 0.171 | −0.23 | −0.42 | −0.47 |
| [15] | NA | Nepal | Numerical | 1000 | 15–60 | 0.001 | −0.073 | −0.20 | −0.132 |
| Present study | $\mathrm{V}_{\mathrm{oc}}=22.05 \mathrm{~V}\\\mathrm{I}_{\mathrm{sc}}=9.81 \mathrm{~A}\\\mathrm{~V}_{\mathrm{mp}}=18 \mathrm{~V}\\\mathrm{I}_{\mathrm{mp}}=9.17 \mathrm{~A}\\\mathrm{P}_{\max }=165 \mathrm{~W}$ | Iraq | Numerical |
1000
800 600 |
25–65 |
0.0815 0.0860 0.0750 |
-0.150 -0.163 -0.158 |
-0.155 -0.155 -0.142 |
-0.260 -0.260 -0.230 |
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| Reference | Isc (A) | Voc (V) | Pmax (W) |
| [36] | Isc = 0.0121*Tc + 7.0092 R2 = 0.9945 |
Voc = −1.2393*Tc + 304.03 R2 = 0.9982 |
P = −0.0077*Tc + 2.44423 R2 = 0.9934 |
| Present study | Isc = 0.008*Tc + 9.38 R2 = 0.9992 |
Voc = −0.034*Tc + 23 R2 = 0.9940 |
P = −0.43*Tc + 180 R2 = 0.9930 |
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The output electrical characteristics of PV module were investigated by numerical and experimental studies. In addition the temperature coefficients of PV module was analyzed numerically. Based on the analyses of the results, the following conclusions can be drawn:
• The numerical results conclude that the maximum power was recorded 165 W at 1000 W/m2 solar irradiance and 25 ℃ PV module temperature.
• The increasing of module temperature has critical effect on the voltage rather than the current.
• The minimum value of power was recorded about 31 W at 200 W/m2 of solar radiation.
• The temperature coefficient was recorded a maximum value with output power about (−0.26) %/℃. While, the temperature coefficient (𝛽) of Voc, (𝜸) of Pmax and (δ) of FF were little changed.
• The experimental results show that the maximum power was recorded 131.2 W at 920 W/m2 solar radiation.
This work was conducted within Middle Technical University and the authors are grateful to the staff of University for their help.
The authors demonstrate that there is no conflict of interest regarding this manuscript.
| [1] | Warlow CP, van Gijn J, Dennis MS, et al. (2008) Stroke: Practical Management (3rd ed.), Oxford: Blackwell Publishing. |
| [2] | Barker WH, Mullooly JP (1997) Stroke in a defined elderly population, 1967–1985. A less lethal and disabling but no less common disease. Stroke 28: 284–290. |
| [3] |
Hendricks HT, van Limbeek J, Geurts AC, et al. (2002) Motor recovery after stroke: a systematic review of the literature. Arch Phys Med Rehabil 83: 1629–1637. doi: 10.1053/apmr.2002.35473
|
| [4] |
De Vries S, Mulder T (2007) Motor imagery and stroke rehabilitation: a critical discussion. J Rehabil Med 39: 5–13. doi: 10.2340/16501977-0020
|
| [5] | Wade DT (1992). Measurement in Neurological Rehabilitation, Oxford: Oxford University Press. |
| [6] |
Pandyan AD, Gregoric M, Barnes MP, et al. (2005) Spasticity: clinical perceptions, neurological realities and meaningful measurement. Disabil Rehabil 27: 2–6. doi: 10.1080/09638280400014576
|
| [7] |
Andrews AW, Bohannon RW (1989) Decreased shoulder range of motion on paretic side after stroke. Phys Ther 69: 768–772. doi: 10.1093/ptj/69.9.768
|
| [8] |
Meskers CG, Koppe PA, Konijnenbelt MH, et. al. (2005) Kinematic alterations in the ipsilateral shoulder of patients with hemiplegia due to stroke. Am J Phys Med Rehabil 84: 97–105. doi: 10.1097/01.PHM.0000150792.26793.E9
|
| [9] |
Chan DYL, Chan CCH, Au DKS (2006) Motor relearning programme for stroke patients: a randomized controlled trial. Clin Rehabil 20: 191–200. doi: 10.1191/0269215506cr930oa
|
| [10] |
Rossini PM, Calautti C, Pauri F, et al. (2003) Post-stroke plastic reorganisation in the adult brain. Lancet Neurol 2: 493–502. doi: 10.1016/S1474-4422(03)00485-X
|
| [11] |
Hubbard IJ, Parsons MW, Neilson C, et al. (2009) Task-specific training evidence for and translation to clinical practice. Occup Ther Int 16: 175–189. doi: 10.1002/oti.275
|
| [12] |
Arya KN, Pandian S, Verma R, et al. (2011) Movement therapy induced neural reorganization and motor recovery in stroke: a review. J Bodyw Mov Ther 15: 528–537. doi: 10.1016/j.jbmt.2011.01.023
|
| [13] | Aichner F, Adelwöhrer C, Haring HP (2002) Rehabilitation approaches to stroke, In: Fleischhacker WW, Brooks DJ, Stroke-vascular Diseases, Vienna: Springer, 59–73. |
| [14] |
Byl N, Roderick J, Mohamed O, et al. (2003) Effectiveness of sensory and motor rehabilitation of the upper limb following the principles of neuroplasticity: patients stable poststroke. Neurorehabil Neural Repair 17: 176–191. doi: 10.1177/0888439003257137
|
| [15] |
Jang SH, Kim YH, Cho SH, et al. (2003) Cortical reorganization induced by task-oriented training in chronic hemiplegic stroke patients. Neuroreport 14: 137–141. doi: 10.1097/00001756-200301200-00025
|
| [16] |
Langhorne P, Coupar F, Pollock A (2009) Motor recovery after stroke: a systematic review. Lancet Neurol 8: 741–754. doi: 10.1016/S1474-4422(09)70150-4
|
| [17] | Park SW, Kim JH, Yang, YJ (2018) Mental practice for upper limb rehabilitation after stroke: a systematic review and meta-analysis. Int J Rehabil Res 41: 197–203. |
| [18] |
Sun Y, Wei W, Luo Z, et al. (2016) Improving motor imagery practice with synchronous action observation in stroke patients. Top Stroke Rehabil 23: 245–253. doi: 10.1080/10749357.2016.1141472
|
| [19] |
Gatti R, Tettamanti A, Gough PM, et al. (2013) Action observation versus motor imagery in learning a complex motor task: a short review of literature and a kinematics study. Neurosci Lett 540: 37–42. doi: 10.1016/j.neulet.2012.11.039
|
| [20] | Neuman B, Gray R (2013) A direct comparison of the effects of imagery and action observation on hitting performance. Movement Sport Sci: Sci Motricité 79: 11–21. |
| [21] |
Guillot A, Collet C (2008) Construction of the motor imagery integrative model in sport: a review and theoretical investigation of motor imagery use. Int Rev Sport Exer P 1: 31–44. doi: 10.1080/17509840701823139
|
| [22] |
Hardwick RM, Caspers S, Eickhoff SB, et al. (2018) Neural Correlates of Action: Comparing Meta-Analyses of Imagery, Observation, and Execution. Neurosci Biobehav Rev 94: 31–44. doi: 10.1016/j.neubiorev.2018.08.003
|
| [23] | Jeannerod M (2006) Motor Cognition, Oxford: Oxford University Press. |
| [24] | Vogt S, Di Rienzo F, Collet C, et al. (2013) Multiple roles of motor imagery during action observation. Front Hum Neurosci 7: 807. |
| [25] |
Rizzolatti G, Sinigaglia C (2010) The functional role of the parieto-frontal mirror circuit: interpretations and misinterpretations. Nat Rev Neurosci 11: 264–274. doi: 10.1038/nrn2805
|
| [26] |
Vogt S, Thomaschke R (2007) From visuo-motor interactions to imitation learning: behavioural and brain imaging studies. J Sports Sci 25: 497–517. doi: 10.1080/02640410600946779
|
| [27] |
Buccino G (2014) Action observation treatment: a novel tool in neurorehabilitation. Philos Trans R Soc Lond B Biol Sci 369: 20130185. doi: 10.1098/rstb.2013.0185
|
| [28] |
Ertelt D, Small S, Solodkin A, et al. (2007) Action observation has a positive impact on rehabilitation of motor deficits after stroke. Neuroimage 36: 164–173. doi: 10.1016/j.neuroimage.2007.03.043
|
| [29] |
Franceschini M, Ceravolo MG, Agosti M, et al. (2012) Clinical relevance of action observation in upper-limb stroke rehabilitation: a possible role in recovery of functional dexterity. A randomized clinical trial. Neurorehabil Neural Repair 26: 456–462. doi: 10.1177/1545968311427406
|
| [30] | Zhang JJQ, Fong KNK, Welage N, et al. (2018) The activation of the mirror neuron system during action observation and action execution with mirror visual feedback in stroke: a systematic review. Neural Plast 2018: 2321045. |
| [31] |
Cumming J, Eaves DL (2018) The nature, measurement, and development of imagery ability. Imagin Cog Pers 37: 375–393. doi: 10.1177/0276236617752439
|
| [32] |
Eaves DL, Emerson JR, Binks JA, et al. (2018) Imagery ability: the individual difference gradient and novel training methods (Commentary on Kraeutner et al. (2018)) Eur J Neurosci 47: 1219–1220. doi: 10.1111/ejn.13928
|
| [33] | Eaves DL, Riach M, Holmes PS, et al. (2016) Motor imagery during action observation: A brief review of evidence, theory and future research opportunities. Front Neurosci 10: 514. |
| [34] |
Stinear CM, Byblow WD, Steyvers M, et al. (2006) Kinesthetic, but not visual, motor imagery modulates corticomotor excitability. Exp Brain Res 168: 157–164. doi: 10.1007/s00221-005-0078-y
|
| [35] | De Vries S, Tepper M, Feenstra W, et al. (2013) Motor imagery ability in stroke patients: the relationship between implicit and explicit motor imagery measures. Front Hum Neurosci 7: 790. |
| [36] | Braun S, Kleynen M, van Heel T, et al. (2013) The effects of mental practice in neurological rehabilitation; a systematic review and meta-analysis. Front Hum Neurosci 7: 390. |
| [37] |
Pascual-Leone A, Nguyet D, Cohen LG, et al. (1995) Modulation of muscle responses evoked by transcranial magnetic stimulation during the acquisition of new fine motor skills. J Neurophysiol 74: 1037–1045. doi: 10.1152/jn.1995.74.3.1037
|
| [38] |
Jackson PL, Lafleur MF, Malouin F, et al. (2003) Functional cerebral reorganization following motor sequence learning through mental practice with motor imagery. Neuroimage 20: 1171–1180. doi: 10.1016/S1053-8119(03)00369-0
|
| [39] | Sharma N, Baron JC, Rowe JB (2009) Motor imagery after stroke: relating outcome to motor network connectivity. Ann Neurol: Official J American Neurol Assoc Child Neurol Soc 66: 604–616. |
| [40] | Grosprêtre S, Lebon F, Papaxanthis C, et al. (2018) Spinal plasticity with motor imagery practice. J Physiol. |
| [41] |
Zimmermann-Schlatter A, Schuster C, Puhan MA, et al. (2008) Efficacy of motor imagery in post-stroke rehabilitation: a systematic review. J Neuroeng Rehabil 5: 8. doi: 10.1186/1743-0003-5-8
|
| [42] |
Machado S, Lattari E, de Sa AS, et al. (2015) Is mental practice an effective adjunct therapeutic strategy for upper limb motor restoration after stroke? A systematic review and meta-analysis. CNS Neurol Disord Drug Targets 14: 567–575. doi: 10.2174/1871527314666150429112702
|
| [43] | Winstein CJ, Stein J, Arena R, et al. (2016) Guidelines for adult stroke rehabilitation and recovery: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 47: e98–e169. |
| [44] |
Hebert D, Lindsay MP, McIntyre A, et al. (2016) Canadian stroke best practice recommendations: stroke rehabilitation practice guidelines, update 2015. Int J Stroke 11: 459–484. doi: 10.1177/1747493016643553
|
| [45] |
Bovend'Eerdt TJ, Dawes H, Sackley C, et al. (2012) Practical research-based guidance for motor imagery practice in neurorehabilitation. Disabil Rehabil 34: 2192–2200. doi: 10.3109/09638288.2012.676703
|
| [46] |
Page SJ, Fulk GD, Boyne P (2012) Clinically important differences for the upper-extremity Fugl-Meyer scale in people with minimal to moderate impairment due to chronic stroke. Phys Ther 92: 791–798. doi: 10.2522/ptj.20110009
|
| [47] |
Tani M, Ono Y, Matsubara M, et al. (2018). Action observation facilitates motor cortical activity in patients with stroke and hemiplegia. Neurosci Res 133: 7–14. doi: 10.1016/j.neures.2017.10.002
|
| [48] |
Burianová H, Marstaller L, Sowman P, et al. (2013) Multimodal functional imaging of motor imagery using a novel paradigm. Neuroimage 71: 50–58. doi: 10.1016/j.neuroimage.2013.01.001
|
| [49] | Kraeutner SN, McWhinney SR, Solomon JP, et al. (2018) Experience modulates motor imagery-based brain activity. Eur J Neurosci 47: 1221–1229. |
| [50] |
Bar RJ, DeSouza JF (2016) Tracking plasticity: effects of long-term rehearsal in expert dancers encoding music to movement. PloS One 11: e0147731. doi: 10.1371/journal.pone.0147731
|
| [51] |
Lacourse MG, Orr EL, Cramer SC, et al. (2005) Brain activation during execution and motor imagery of novel and skilled sequential hand movements. Neuroimage 27: 505–519. doi: 10.1016/j.neuroimage.2005.04.025
|
| [52] |
Macuga KL, Frey SH (2012) Neural representations involved in observed, imagined, and imitated actions are dissociable and hierarchically organized. Neuroimage 59: 2798–2807. doi: 10.1016/j.neuroimage.2011.09.083
|
| [53] |
Nedelko V, Hassa T, Hamzei F, et al. (2012) Action imagery combined with action observation activates more corticomotor regions than action observation alone. J Neurol Phys Ther 36: 182–188. doi: 10.1097/NPT.0b013e318272cad1
|
| [54] |
Villiger M, Estévez N, Hepp-Reymond MC, et al. (2013) Enhanced activation of motor execution networks using action observation combined with imagination of lower limb movements. PLoS One 8: e72403. doi: 10.1371/journal.pone.0072403
|
| [55] |
Taube W, Mouthon M, Leukel C, et al. (2015) Brain activity during observation and motor imagery of different balance tasks: an fMRI study. Cortex 64: 102–114. doi: 10.1016/j.cortex.2014.09.022
|
| [56] |
Bian Y, Qi H, Zhao L, et al. (2018) Improvements in event-related desynchronization and classification performance of motor imagery using instructive dynamic guidance and complex tasks. Comput Biol Med 96: 266–273. doi: 10.1016/j.compbiomed.2018.03.018
|
| [57] |
Berends HI, Wolkorte R, Ijzerman MJ, et al. (2013) Differential cortical activation during observation and observation-and-imagination. Exp Brain Res 229: 337–345. doi: 10.1007/s00221-013-3571-8
|
| [58] |
Eaves DL, Behmer LP, Vogt S (2016) EEG and behavioural correlates of different forms of motor imagery during action observation in rhythmical actions. Brain Cogn 106: 90–103. doi: 10.1016/j.bandc.2016.04.013
|
| [59] |
Neuper C, Scherer R, Wriessnegger S, et al. (2009) Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. Clin Neurophysiol 120: 239–247. doi: 10.1016/j.clinph.2008.11.015
|
| [60] |
Mouthon A, Ruffieux J, Wälchli M, et al. (2015) Task-dependent changes of corticospinal excitability during observation and motor imagery of balance tasks. Neuroscience 303: 535–543. doi: 10.1016/j.neuroscience.2015.07.031
|
| [61] |
Sakamoto M, Muraoka T, Mizuguchi N, et al. (2009) Combining observation and imagery of an action enhances human corticospinal excitability. Neurosci Res 65: 23–27. doi: 10.1016/j.neures.2009.05.003
|
| [62] |
Tsukazaki I, Uehara K, Morishita T, et al. (2012) Effect of observation combined with motor imagery of a skilled hand-motor task on motor cortical excitability: difference between novice and expert. Neurosci Lett 518: 96–100. doi: 10.1016/j.neulet.2012.04.061
|
| [63] | Wright DJ, Williams J, Holmes PS (2014) Combined action observation and imagery facilitates corticospinal excitability. Front Hum Neurosci 8: 951. |
| [64] | Wright DJ, McCormick SA, Williams J, et al. (2016) Viewing instructions accompanying action observation modulate corticospinal excitability. Front Hum Neurosci 10: 17. |
| [65] |
Wright DJ, Wood G, Eaves DL, et al. (2018) Corticospinal excitability is facilitated by combined action observation and motor imagery of a basketball free throw. Psychol Sport Exerc 39: 114–121. doi: 10.1016/j.psychsport.2018.08.006
|
| [66] | Taube W, Lorch M, Zeiter S, et al. (2014) Non-physical practice improves task performance in an unstable, perturbed environment: motor imagery and observational balance training. Front Hum Neurosci 8: 972. |
| [67] |
Bek J, Poliakoff E, Marshall H, et al. (2016) Enhancing voluntary imitation through attention and motor imagery. Exp Brain Res 234: 1819–1828. doi: 10.1007/s00221-016-4570-3
|
| [68] | Eaves DL, Haythornthwaite L, Vogt S (2014) Motor imagery during action observation modulates automatic imitation effects in rhythmical actions. Front Hum Neurosci 8: 28. |
| [69] |
Scott M, Taylor S, Chesterton P, et al. (2018) Motor imagery during action observation increases eccentric hamstring force: an acute non-physical intervention. Disabil Rehabil 40: 1443–1451. doi: 10.1080/09638288.2017.1300333
|
| [70] |
Romano-Smith S, Wood G, Wright DJ, et al. (2018) Simultaneous and alternate action observation and motor imagery combinations improve aiming performance. Psychol Sport Exerc 38: 100–106. doi: 10.1016/j.psychsport.2018.06.003
|
| [71] |
Marusic U, Giordani B, Moffat SD, et al. (2018) Computerized cognitive training during physical inactivity improves executive functioning in older adults. Aging Neuropsychol Cogn 25: 49–69. doi: 10.1080/13825585.2016.1263724
|
| [72] | Bek J, Gowen E, Vogt S, et al. (2018) Combined action observation and motor imagery influences hand movement amplitude in Parkinson's disease. Parkinsonism Relat Disord. |
| [73] |
Cisek P, Kalaska JF (2010) Neural mechanisms for interacting with a world full of action choices. Annu Rev Neurosci 33: 269–298. doi: 10.1146/annurev.neuro.051508.135409
|
| [74] |
Eaves DL, Turgeon M, Vogt S (2012) Automatic imitation in rhythmical actions: kinematic fidelity and the effects of compatibility, delay, and visual monitoring. PLoS One 7: e46728. doi: 10.1371/journal.pone.0046728
|
| [75] |
McInnes K, Friesen C, Boe S (2016) Specific brain lesions impair explicit motor imagery ability: a systematic review of the evidence. Arch Phys Med Rehabil 97: 478–489. doi: 10.1016/j.apmr.2015.07.012
|
| [76] | Oostra KM, Van Bladel A, Vanhoonacker AC, et al. (2016) Damage to fronto-parietal networks impairs motor imagery ability after stroke: a voxel-based lesion symptom mapping study. Front Behav Neurosci 10: 5. |
| [77] |
Evans C, Edwards MG, Taylor LJ, et al. (2016) Perceptual decisions regarding object manipulation are selectively impaired in apraxia or when tDCS is applied over the left IPL. Neuropsychologia 86: 153–166. doi: 10.1016/j.neuropsychologia.2016.04.020
|
| [78] |
Kraeutner SN, Keeler LT, Boe SG (2016) Motor imagery-based skill acquisition disrupted following rTMS of the inferior parietal lobule. Exp Brain Res 234: 397–407. doi: 10.1007/s00221-015-4472-9
|
| [79] |
Pinter MM, Brainin M (2012) Rehabilitation after stroke in older people. Maturitas 71: 104–108. doi: 10.1016/j.maturitas.2011.11.011
|
| [80] |
Pinter MM (2015) Rehabilitation in Stroke Patients: Focusing on the Future. Hamdan Medical J, 8: 321–330. doi: 10.7707/hmj.481
|
| [81] |
Heyes C (2011) Automatic imitation. Psychol Bull 137: 463–483. doi: 10.1037/a0022288
|
| [82] |
Holmes PS, Collins DJ (2001) The PETTLEP approach to motor imagery: a functional equivalence model for sport psychologists. J Appl Sport Psychol 13: 60–83. doi: 10.1080/10413200109339004
|
| [83] |
Cumming J, Cooley SJ, Anuar N, et al. (2017) Developing imagery ability effectively: a guide to layered stimulus response training. J Sport Psychol Action 8: 23–33. doi: 10.1080/21520704.2016.1205698
|
| [84] |
Lang PJ (1977) Imagery in therapy: an information processing analysis of fear. Behav Ther 8: 862–886. doi: 10.1016/S0005-7894(77)80157-3
|
| [85] |
Lang PJ (1979) A bio-informational theory of emotional imagery. Psychophysiology 16: 495–512. doi: 10.1111/j.1469-8986.1979.tb01511.x
|
| [86] | Ventola CL (2014) Mobile devices and apps for health care professionals: uses and benefits. Pharmacy Theraputics 39: 356–364. |
| [87] |
Mosa AS, Yoo I, Sheets L (2012) A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak 12: 67. doi: 10.1186/1472-6947-12-67
|
| [88] |
Sureshkumar K, Murthy GV, Munuswamy S, et al. (2015) 'Care for Stroke', a web-based, smartphone-enabled educational intervention for management of physical disabilities following stroke: feasibility in the Indian context. BMJ Innov 1: 127–136. doi: 10.1136/bmjinnov-2015-000056
|
| [89] | Goodney A, Jung J, Needham S, et al. (2012) Dr Droid: assisting stroke rehabilitation using mobile phones, International Conference on Mobile Computing, Applications and Services, Berlin, Heidelberg: Springer, 231–242. |
| [90] |
Carr JH, Shepherd RB (2012) An excellent initiative. J Physiother 58: 134–135. doi: 10.1016/S1836-9553(12)70101-7
|
| [91] | Intercollegiate Stroke Working Party (2012) National Clinical Guideline for Stroke, 4th edition, London: Royal College of Physicians. |
| [92] |
Wolpaw JR, Birbaumer N, McFarland DJ, et al. (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113: 767–791. doi: 10.1016/S1388-2457(02)00057-3
|
| [93] |
Cervera MA, Soekadar SR, Ushiba J, et al. (2018) Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann Clin Transl Neurol 5: 651–663. doi: 10.1002/acn3.544
|
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| Parameter | Value |
| Dimensions | 149 × 67 cm |
| Pmax | 165 W |
| Isc | 9.81 A |
| Voc | 22.05 V |
| Imp | 9.17 A |
| Vmp | 18 V |
| ƞ | 20 |
DownLoad:
CSV
| Solar radiation W/m2 | Linear models and temperature coefficients | |||
| Isc (A) | Voc (V) | FF (%) | P (W) | |
| 1000 | $\mathrm{I}_{\mathrm{sc}}=0.008 * T_{\mathrm{c}}+9.38\\ \alpha=0.0815 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.9992$ | $\mathrm{V}_{\mathrm{oc}}=-0.034 * \mathrm{~T}_{\mathrm{c}}+23\\ \beta=-0.15 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.994$ | $\mathrm{FF}=-0.120 * \mathrm{~T}_{\mathrm{c}}+80\\ \delta=-0.155 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.989$ | $\mathrm{P}=-0.43^* \mathrm{~T}_{\mathrm{c}}+180\\ \gamma=-0.26 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.993$ |
| 800 | $\mathrm{I}_{\mathrm{sc}}=0.0085^* \mathrm{~T}_{\mathrm{c}}+9.285\\ \alpha=0.086 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.9996$ | $\mathrm{~V}_{\mathrm{oc}}=-0.036 * \mathrm{~T}_{\mathrm{c}}+23\\ \beta=-0.163 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.987$ | $\mathrm{FF}=-0.12 * \mathrm{~T}_{\mathrm{c}}+79\\ \delta=-0.155 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.979$ | $\mathrm{P}=-0.43^* \mathrm{~T}_{\mathrm{c}}+170\\ \gamma=-0.26 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.997$ |
| 600 | $\mathrm{I}_{\mathrm{sc}}=0.0074 * \mathrm{~T}_{\mathrm{c}}+9217\\ \alpha=0.075 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.9993$ | $\mathrm{I}_{\mathrm{sc}}=-0.035 * \mathrm{~T}_{\mathrm{c}}+23\\ \beta=-0.158 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.986$ | $\mathrm{FF}=-0.11 * \mathrm{~T}_{\mathrm{c}}+78\\ \delta=-0.142 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.989$ | $\mathrm{P}=-0.38 * \mathrm{~T}_{\mathrm{c}}+160\\ \boldsymbol{\gamma}=-0.23 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.988$ |
DownLoad:
CSV
| Ref. | Technical data | Country | Type of study | Solar radiationW/m2 | Temp. Module℃ | Temperature coefficients %/℃ | |||
| α (Isc) | β (Voc) | δ (FF) | 𝜸 (Pmax) | ||||||
| [36] | $\mathrm{V}_{\mathrm{oc}}=44.9 \mathrm{~V}\\ \mathrm{Isc}=5.75 \mathrm{~A}\\ \mathrm{~V}_{\mathrm{mp}}=36.2 \mathrm{~V}\\ \mathrm{I}_{\mathrm{mp}}=5.11 \mathrm{~A}\\ \mathrm{P}_{\max }=80 \mathrm{~W}$ | Thailand | Exp.Outdoor | 1000 | 40–65 | 0.08181 | −0.05185 | NA | −0.00038 |
| [10] | $V_\mathrm{o c}=0-50 \mathrm{~V}\\ I_{\mathrm{sc}}=0-15 \mathrm{~A}\\\mathrm{P}_{\max }>10 \mathrm{~W}$ | Norway | Exp.Outdoor | > 400 | 10–70 | 0.171 | −0.23 | −0.42 | −0.47 |
| [15] | NA | Nepal | Numerical | 1000 | 15–60 | 0.001 | −0.073 | −0.20 | −0.132 |
| Present study | $\mathrm{V}_{\mathrm{oc}}=22.05 \mathrm{~V}\\\mathrm{I}_{\mathrm{sc}}=9.81 \mathrm{~A}\\\mathrm{~V}_{\mathrm{mp}}=18 \mathrm{~V}\\\mathrm{I}_{\mathrm{mp}}=9.17 \mathrm{~A}\\\mathrm{P}_{\max }=165 \mathrm{~W}$ | Iraq | Numerical |
1000
800 600 |
25–65 |
0.0815 0.0860 0.0750 |
-0.150 -0.163 -0.158 |
-0.155 -0.155 -0.142 |
-0.260 -0.260 -0.230 |
DownLoad:
CSV
| Reference | Isc (A) | Voc (V) | Pmax (W) |
| [36] | Isc = 0.0121*Tc + 7.0092 R2 = 0.9945 |
Voc = −1.2393*Tc + 304.03 R2 = 0.9982 |
P = −0.0077*Tc + 2.44423 R2 = 0.9934 |
| Present study | Isc = 0.008*Tc + 9.38 R2 = 0.9992 |
Voc = −0.034*Tc + 23 R2 = 0.9940 |
P = −0.43*Tc + 180 R2 = 0.9930 |
DownLoad:
CSV
| Parameter | Value |
| Dimensions | 149 × 67 cm |
| Pmax | 165 W |
| Isc | 9.81 A |
| Voc | 22.05 V |
| Imp | 9.17 A |
| Vmp | 18 V |
| ƞ | 20 |
| Solar radiation W/m2 | Linear models and temperature coefficients | |||
| Isc (A) | Voc (V) | FF (%) | P (W) | |
| 1000 | $\mathrm{I}_{\mathrm{sc}}=0.008 * T_{\mathrm{c}}+9.38\\ \alpha=0.0815 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.9992$ | $\mathrm{V}_{\mathrm{oc}}=-0.034 * \mathrm{~T}_{\mathrm{c}}+23\\ \beta=-0.15 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.994$ | $\mathrm{FF}=-0.120 * \mathrm{~T}_{\mathrm{c}}+80\\ \delta=-0.155 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.989$ | $\mathrm{P}=-0.43^* \mathrm{~T}_{\mathrm{c}}+180\\ \gamma=-0.26 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.993$ |
| 800 | $\mathrm{I}_{\mathrm{sc}}=0.0085^* \mathrm{~T}_{\mathrm{c}}+9.285\\ \alpha=0.086 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.9996$ | $\mathrm{~V}_{\mathrm{oc}}=-0.036 * \mathrm{~T}_{\mathrm{c}}+23\\ \beta=-0.163 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.987$ | $\mathrm{FF}=-0.12 * \mathrm{~T}_{\mathrm{c}}+79\\ \delta=-0.155 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.979$ | $\mathrm{P}=-0.43^* \mathrm{~T}_{\mathrm{c}}+170\\ \gamma=-0.26 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.997$ |
| 600 | $\mathrm{I}_{\mathrm{sc}}=0.0074 * \mathrm{~T}_{\mathrm{c}}+9217\\ \alpha=0.075 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.9993$ | $\mathrm{I}_{\mathrm{sc}}=-0.035 * \mathrm{~T}_{\mathrm{c}}+23\\ \beta=-0.158 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.986$ | $\mathrm{FF}=-0.11 * \mathrm{~T}_{\mathrm{c}}+78\\ \delta=-0.142 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.989$ | $\mathrm{P}=-0.38 * \mathrm{~T}_{\mathrm{c}}+160\\ \boldsymbol{\gamma}=-0.23 \% /{ }^{\circ} \mathrm{C}\\ \mathrm{R}^2=0.988$ |
| Ref. | Technical data | Country | Type of study | Solar radiationW/m2 | Temp. Module℃ | Temperature coefficients %/℃ | |||
| α (Isc) | β (Voc) | δ (FF) | 𝜸 (Pmax) | ||||||
| [36] | $\mathrm{V}_{\mathrm{oc}}=44.9 \mathrm{~V}\\ \mathrm{Isc}=5.75 \mathrm{~A}\\ \mathrm{~V}_{\mathrm{mp}}=36.2 \mathrm{~V}\\ \mathrm{I}_{\mathrm{mp}}=5.11 \mathrm{~A}\\ \mathrm{P}_{\max }=80 \mathrm{~W}$ | Thailand | Exp.Outdoor | 1000 | 40–65 | 0.08181 | −0.05185 | NA | −0.00038 |
| [10] | $V_\mathrm{o c}=0-50 \mathrm{~V}\\ I_{\mathrm{sc}}=0-15 \mathrm{~A}\\\mathrm{P}_{\max }>10 \mathrm{~W}$ | Norway | Exp.Outdoor | > 400 | 10–70 | 0.171 | −0.23 | −0.42 | −0.47 |
| [15] | NA | Nepal | Numerical | 1000 | 15–60 | 0.001 | −0.073 | −0.20 | −0.132 |
| Present study | $\mathrm{V}_{\mathrm{oc}}=22.05 \mathrm{~V}\\\mathrm{I}_{\mathrm{sc}}=9.81 \mathrm{~A}\\\mathrm{~V}_{\mathrm{mp}}=18 \mathrm{~V}\\\mathrm{I}_{\mathrm{mp}}=9.17 \mathrm{~A}\\\mathrm{P}_{\max }=165 \mathrm{~W}$ | Iraq | Numerical |
1000
800 600 |
25–65 |
0.0815 0.0860 0.0750 |
-0.150 -0.163 -0.158 |
-0.155 -0.155 -0.142 |
-0.260 -0.260 -0.230 |
| Reference | Isc (A) | Voc (V) | Pmax (W) |
| [36] | Isc = 0.0121*Tc + 7.0092 R2 = 0.9945 |
Voc = −1.2393*Tc + 304.03 R2 = 0.9982 |
P = −0.0077*Tc + 2.44423 R2 = 0.9934 |
| Present study | Isc = 0.008*Tc + 9.38 R2 = 0.9992 |
Voc = −0.034*Tc + 23 R2 = 0.9940 |
P = −0.43*Tc + 180 R2 = 0.9930 |
