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Monitoring system to evaluate the outdoor performance of solar devices considering the power rating conditions
Solar Energy 194 (2019) 79–85
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Monitoring system to evaluate the outdoor performance of solar devices considering the power rating conditions
T
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Esteban Velillaa,b, Juan B. Canob, , Franklin Jaramilloa a b
Centro de Investigación, Innovación y Desarrollo de Materiales – CIDEMAT, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia Grupo en Manejo Eficiente de la Energía, GIMEL, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
A R T I C LE I N FO
A B S T R A C T
Keywords: Monitoring system Power rating conditions IEC 61853 Solar devices Outdoor performance
In order to evaluate the impact of irradiance and temperature on the outdoor performance of solar devices, a monitoring system including electronic analyzers for measuring the I-V curve and a data management to store, synchronize and process the electrical and weather records were developed. With this system, the average performance as a function of irradiance and temperature of commercial solar panels was obtained in natural sunlight without tracker considering up to 4700 h of exposure and sampling time of 1 min. Besides, the data were processed considering the irradiance levels defined by each power rating conditions suggested by IEC 61853. The results indicated that in tropical weather the Standard Test Conditions (STC) are not as representative to evaluate solar panels, due that an irradiance of 1000 W/m2 rarely occurs at 25 °C temperature of the panel. However, the Nominal Operative Cell Temperature (NOCT) was the most suitable condition to test outdoor performance according to the manufacturer data. Therefore, this condition could be considered to evaluate the status of devices and to define the maintenance guidelines of photovoltaic devices. Finally, the NOCT behavior over time suggested that between 200 and 600 h of exposure are more than enough to characterize and obtain reliable Power Rating Conditions (PRC) under the local conditions.
1. Introduction The performance of solar devices is generally specified at Standard Test Conditions (STC), conditions that rarely occurs in real operation (Dash et al., 2017; Virtuani et al., 2011). In this sense, international standards such as IEC 61853 (IEC 61853-1, 2011), suggests different Power Rating Conditions (PRC) as a function of irradiance and temperature for evaluating photovoltaic modules performance. Therefore, the power delivery of devices can be measured at indoors using a solar simulation and controlling the device temperature or at outdoors in natural sunlight. In the case of outdoor conditions, the time of exposure required to obtain the PRC is a big issue as some required conditions could never happen during the exposure. Moreover, transient effects related to unclear days, seasonal conditions and irradiance fluctuations caused mainly by clouds or shades, could affected the measurements. Besides, the performance depends on location, installation conditions, seasonal variations, shading and soiling effects, among other parameters (Phinikarides et al., 2014; Said et al., 2018). To track the power delivery from solar devices (panels and cells), electronic equipment such as inverters (Visa et al., 2016) and resistive load (Velilla et al., 2014) have been used. However, in these cases, the
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accuracy of the outdoor performance depend mainly on the maximum power point tracking technic implemented (Eltamaly et al., 2018). On other hand, measuring the Current vs Voltage curves (I-V) allowed to perform a full characterization of devices due that not only the maximum power can be extracted, but else, the short circuit current (Isc) and open circuit voltage (Voc). Moreover, these curves have been used to explain partial shading phenomenon and the implications on maximum power (Silvestre and Chouder, 2008), analyze solar cell degradation (Khenkin et al., 2019), estimate the parameters of electrical model (Velilla et al., 2018), among other. To measure the I-V curve of solar devices different hardware techniques have been used (Duran et al., 2008). For instance, a resistor is connected to the device under test (DUT), changing the resistance between a value near to zero (short circuit) and a higher value considered as open circuit (Nikoletatos and Halambalakis, 2018). It is to remark that additional hardware is required to change the resistance value such as potentiometer-motor systems, relay commuted series and parallel resistors arrays, digital potentiometers, among other (Van Dyk et al., 2005). Moreover, MOSFET transistors have been used as variable loads for I-V curve measurement allowing an easier control of the DUT operation point, without intervention of mechanical or electromechanical
Corresponding author. E-mail addresses: [email protected] (E. Velilla), [email protected] (J.B. Cano), [email protected] (F. Jaramillo).
https://doi.org/10.1016/j.solener.2019.10.051 Received 28 June 2019; Received in revised form 13 October 2019; Accepted 21 October 2019 0038-092X/ © 2019 International Solar Energy Society. Published by Elsevier Ltd. All rights reserved.
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operate for voltages up to 8 V and currents up to 3 A (solar cells and solar mini modules).
devices (Hassaine et al., 2014; Papageorgas et al., 2015; Willoughby et al., 2014; Willoughby and Osinowo, 2018). In both cases, heat dissipation is a concern, limiting the applicability at high power solar panels. Furthermore, DC/DC converters are used on I-V curve characterization (Enrique et al., 2005; Faifer et al., 2015), in this case a Pulse Width Modulation (PWM) signal controls the DUT operation point and allows to control the sweep over the I-V curve. However, the complexity and components number of this technique is increased compared to a resistive or transistor load technics. On the other hand, capacitive loads have also been reported to measure the I-V curve (Agroui, 2012; Cano et al., 2015). These types of devices work by connecting a discharged capacitor to the DUT and registering the voltage and current waveforms during the transient response. This technique has several advantages, the capacitor has no heat dissipation issues and it is possible to be used on high power applications. Moreover, the sweep over the curve is achieved by the capacitor dynamics, without using any external mechanism. However, it is not possible to control the direction (from short circuit to open circuit) and speed of the sweep, because it depends on capacitance and DUT characteristics. Finally, it is also possible to use a controlled voltage source to trace I-V curve (Duran et al., 2008), this allows a more complete characterization, allowing to explore the reverse bias region, voltages higher than the open circuit voltage. Also, it is possible to directly control the voltage change rate (V/s rate) and control the sweep direction making it practical for hysteresis measurements (Li et al., 2017). However, this technique involves an additional power supply increasing costs and system complexity. Taking into account the advantages and disadvantages of the aforementioned techniques, and considering that the I-V curve of solar devices depends on the weather variables such as temperature and irradiance, which are not constant and could change abruptly at outdoor conditions by clouds, shades, drops, etc., we implemented a monitoring system that included electronic devices based on portability, low cost and faster response to measure the I-V curve according to the electrical power of the device evaluated and a data management to store, synchronize and process the electrical and atmospheric data. Finally, the data were processed to obtain the impact of irradiance and temperature on the performance and the power rating conditions defined by the standard IEC 61853.
2.1.1. Solar panel analyzer (SPA) Fig. 1a shows the building blocks of device that involved a panel, a capacitive load, solid state relays for charging and discharging control, voltage sensor (AMC1200-Isolation amplifier, Texas Instruments), current sensor (ACS-711 series - Hall effect, Allegro), an analog to digital converter (ADC) and a microcontroller. The control and communication systems were implemented on a 32 bits microcontroller (PIC32MX230F064D, Microchip). For safety reasons, due to voltage and current levels of panel, the isolation between power (load, sensors, relays) and control circuit sections (microcontroller, communications) was implemented, Fig. 1b. Voltage and current signals are digitalized by a sigma-delta analog to digital converter (AD7172-2, Analog devices). Serial port interface communication was implemented between converter and microcontroller. This setup allows sampling times of 0.32 ms for each I-V curve point (total time to sequentially take a voltage sample and a current sample). Sampling time is important to determine the minimum capacitance value required to measure the charging transient. A serial port emulation protocol was implemented over USB, providing a command set to control analyzer functions and retrieve data. Finally, the expansion sensors block allows direct connection to the analog-digital converter of microcontroller, intended for external sensor interface such as irradiance and temperature variables. The SPA was programmed as follow. First, capacitor is discharged by means of a resistor in parallel connected by a solid-state relay. Then, solar panel is keep at open circuit. Second, the Voc of panel is measured. Third, solar panel is connected to capacitor and voltage and current sampling is started. Fourth, the analyzer keeps sampling while a preconfigured time is over. Fifth, the SPA is disconnected from load. Sixth, the capacitor is discharged. Seventh, I-V data is printed via USB port.
2.1.2. Solar cell analyzer (SCA) This analyzer used a controlled voltage supply (potentiostat) to set the voltage on the DUT. The voltage is changed in the interest range of the I-V curve and is digitally controlled through a digital to analog converter, Fig. 2a. The potentiostat circuit was implemented using linear circuits (operational amplifiers), due to the low voltage, current and power of the solar cells, Fig. 2a. Comparing with a switching converter, this implementation has more power losses but is simpler and quicker to implement. Operational amplifiers series OPA548T (Texas Instruments) where selected due to their output current characteristics. A trans-resistance amplifier configuration is used as current sensor considering the same operational amplifiers. A differential amplifier (LT1167, Linear Technologies) was used as voltage sensor, Fig. 2b. Notice that this prototype does not have any isolation between cell and control circuits. A digital to analog converter (TLV5638, Texas Instruments) was used for generation of the voltage references for solar cell. Signal digitalization, processing, sensor expansion and communications were carried out using the same components described for panel analyzer. The SPC was programmed as follows. First, the DUT is disconnected from the analyzer and Voc is measured. Second, the start voltage is configured on the DAC and the solar device is connected to register the voltage and current. Third, the DAC voltage is linearly increased or decreased, and measurements are registered. Four, the process continues until reach the end voltage. Fifth, the DUT is disconnected from analyzer. Sixth, current and voltage data are sent via USB port. Other information related to the solar analyzer is shown in supporting information, Tables S1 and S2.
2. Methodology With the aim to obtain the outdoor performance of solar devices, a monitoring system that included the solar analyzers and the data management was developed. Hence, two different I-V tracing devices focused on in-situ outdoor measurement capabilities such as low cost, portability, faster response and scalability (Section 2.1), and a weather system for measuring the irradiance levels and temperatures were implemented (Section 2.2.2). Moreover, a data management system to store and process the electrical (I-V curve) and weather data was developed. The data management included processing algorithms in order to minimize the effects related to unclear days, short term irradiance fluctuations or atypical data and obtain the outdoor performance as function of irradiance levels and temperature. Finally, the system was tested by measuring different commercial solar devices and validated according to manufacturer datasheet. 2.1. Solar analyzer (SA) Two different types of I-V curve tracers (SA) were implemented. One used the capacitive load technique due to simplicity, power dissipation and cost concerns. This prototype is intended for DUTs that operate for voltages up to 250 V and currents up to 12.5 A (solar panels, Fig. 1). Other used a four quadrant DC supply due to greater flexibility on sweep direction and speed. This prototype is intended for DUT that 80
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Fig. 1. Panel analyzer. (a) Block diagram. (b) Printed Circuit Board.
2.2. Data management 2.2.1. Electrical performance measurement To measure the I-V curve of solar devices, the Solar Analyzer (SA) included an embedded computer (The Raspberry Pi 3) connected to each SPA or SCA, Fig. 3. Raspberry Pi runs Raspbian OS and executes the following tasks:
• System date and time are keeping updated using the Network Time • • •
Protocol Daemon (NTPD). It guarantees low error on measurement time stamps. An SSH (Secure Shell) interface allows remote access for modification of system configuration settings (as measurement frequency, sensor calibration, etc) or scripts edition. Cron service (Linux task programmer) is configured to execute every minute the Python script to communicate the Raspberry with the SPA or SCA and start the measurement process. The registers (I-V data) are saved in a CSV file including the timestamp. Finally, CSV files are synchronized every hour with the remote server, using the Rsync service over SSH, Fig. 4.
Fig. 3. Final SA for on-field installation.
It is to remark that before any on-field testing, current and voltage sensors are calibrated to guarantee measurement accuracy of SA.
Sensor data were collected using a Sensor Box Datalogger (FRONIUS International GMBH). Sensor data were sent via Ethernet communication to the Raspberries for local storage on CSV files (adding the timestamp). Finally, Raspberry synchronized the records related to irradiance levels and temperatures every hour with the remote server using Rsync service over SSH. As mentioned before, electrical (I-V) and weather (irradiance and temperatures) data are uploaded to the remote server, not only to
2.2.2. Weather data measurement A complete characterization of solar devices requires a weather monitoring system. Thus, solar irradiance and temperature sensors where implemented. Solar irradiance was measured using the Spektron 210 (TRITEC international) sensor. Ambient and panel/cell temperatures were measured using PT-1000 sensors (TRITEC international).
Fig. 2. Solar cells analyzer. (a) Block diagram. (b) Printed Circuit Board. 81
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Fig. 4. Monitoring system to evaluate outdoor performance.
provide backup, but also to centralize monitoring and processing operations, Fig. 4. Notice that the system can be easily scaled to include more SA.
2.2.3. Data processing From the electrical data stored in the server and measured through the I-V curve by the SA, the short circuit current (Isc), open circuit voltage (Voc) and the maximum power (Pmax) of devices were extracted. Then, a Python script is executed to link the electrical data with the irradiance levels and temperatures records using the captured timestamps, then a database by device is created (see Fig. S1). After that, the databases are processed based on the linear relationship between the electrical power and irradiance according to the linearity determination criteria, in order to minimize the effects related to unclear days, short term irradiance fluctuations or atypical data (Velilla et al., 2019), Fig. 5. According to IEC 61853-1, different PRC such as Standard Test Condition (STC, measure at 1000 W/m2 of irradiance and 25 °C of cell temperature), Nominal Operating Cell Temperature (NOCT, measure at 800 W/m2 of irradiance and 20 °C of ambient temperature), Low Irradiance Condition (LIC, measured at 200 W/m2 of irradiance and 25 °C of cell temperature), High Temperature Condition (HTC, measure at 1000 W/m2 of irradiance and 75 °C of cell temperature) and Low Temperature Condition (LTC, measure at 500 W/m2 of irradiance and 15 °C of cell temperature) have to be measured during the evaluation time or extrapolated to determine the impact of irradiance and temperature on the performance. However, this impact could be graphically observed through maps, including the most representatives operative points measured during the exposure time and the PRCs. Additionally, the data could be processed to perform different temporary analyzes, such as the power delivery over time (Ramirez et al., 2019).
Fig. 5. Linear relationship between maximum power and irradiance of Sharp panel. The gray dots indicated the measurements registered during the exposure and the linear polynomial shown in the legend corresponded to the fitted data. On the top is included the coefficient of determination of the fitted process, suggesting a linear relationship between both variables. The dots close to the fitted line corresponded to the filtered data.
3. Results To evaluate the outdoor performance of solar devices, the SA were linked with the monitoring system that included the data management implemented. Then, three commercial solar devices were monitored 82
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Fig. 6. Power delivery map as a function of irradiance and panel temperature. (a) Sharp NU-RC290 monitored between 2018-07-05 and 2019-01-20, (b) Yingli YL275D-30b monitored between 2018-11-22 and 2019-04-24, and (c) HIT VBHN330SJ47 monitored between 2019-02-23 and 2019-06-03. The maximum value of power delivery is shown on top of each map, and the colorbar indicates the values of Pmax.
Fig. 7. Power rating conditions over time for evaluated panels. Data for: (a) Sharp, (b) Yingli and (c) HIT. The block colors are related to the PRC as follows: green to 1000 W/m2 (STC), blue to 800 W/m2 (NOCT), yellow to 500 W/m2 (LTI) and gray to 200 W/m2 (LIC). Table 1 STC and NOCT conditions for solar devices supplied by manufacturer and obtained during the outdoor test. Devices
Sharp NU-RC290
Yingli YL275D-30b
HIT VBHN330SJ47
Variables
Pmax (W) Isc (A) Voc (V) Pmax (W) Isc (A) Voc (V) Pmax (W) Isc (A) Voc (V)
STC (1000 W/m2)
NOCT (800 W/m2)
Data sheet
Mean
std
Error (%)
Data sheet
Mean
std
Error (%)
290 9.8 39.3 275 9.34 38.9 330 6.07 69.7
257.33 10.26 35.67 243.12 9.62 34.15 315.82 6.56 64.35
8.31 0.39 0.50 8.34 0.37 0.58 9.44 0.26 0.80
11.26 −4.69 9.23 11.59 −2.99 12.21 4.29 −8.07 −0.51
212 7.93 36.2 200.6 7.55 35.90 247.2 4.91 65.1
212.78 8.16 35.78 200.56 7.78 34.18 253.61 5.15 64.76
6.78 0.38 0.44 7.03 0.33 0.51 9.03 0.25 0.63
−0.36 −2.34 1.16 0.02 −3.04 4.79 −2.59 −4.89 0.52
performance maps as a function of the irradiance and temperature of devices considering the entire exposure time and processing the data with the linearity determination criteria. In spite of the maps of power delivery allowed us to graphically visualize the impact of irradiance and temperature on the devices performance, two main points have to be remarked. First, this performance is related to the most frequency conditions happened during the exposure time. Second, due to the setup was carried out on tropical weather conditions, it is to expect that some PRC such as STC, HTC and LTC rarely occur. Therefore, some operative points or PRC could not be included in the maps due that these could not be considered as statistically representative. Nevertheless, due to the exposure time exceeding 2600 h and the sampling time between measurements was 1 min, the
between august of 2018 and June of 2019 considering different period of time and natural sunlight without tracker on the terrace of the University Research Center - SIU from the Universidad de Antioquia (6° 15′ 38″ N 75° 34′ 05″W), facing south at fixed angle of 13° (see Fig. S2). Hence, the monitoring system was setup to record every minute during the light-hours the electrical variables (I-V curves) and weather data (irradiance levels and temperatures). Moreover, the files with electrical and weather data were synchronized every 24 h to back-up the records in the server and update the databases of devices evaluated. After that, the databases can be processed at any time considering the linearity determination criteria between the irradiance and power to minimize transient effects related to irradiance fluctuations or atypical data (Fig. 5 and Figs. S3-6). For instance, in Fig. 6 are showed the 83
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Based on the errors on Table 1, it is remarked that the NOCT was the most suitable power rating condition that represent the outdoor performance, being the error within the range ± 5% for the variables analyzed (see Table 1 and Tables S3-5). Moreover, normalizing the NOCT mean values with respect to the manufacturer data (Fig. 8), it was observed that the most part of exposure time the devices were within the range of ± 5%. Therefore, the evaluation of the NOCT at outdoor conditions instead of the STC as suggested the IEC 61-583-1, it could be used not only to check the status of devices with respect to manufacturer data, but also, to define cleaning maintenance and to identify the most suitable conditions of the panels. In this last case, normalizing the PRC with respect to the NOCT supplied by manufacturer (Table S6), it was possible to identify that the HIT panels performed better at high irradiance levels, while other panels worked better at lower irradiances. From this way, a most trustfully comparison between panels at outdoor conditions can be performed indeed to select the panel or technology that could be installed depending on the most representative irradiance levels of the location. Finally, the monitoring system could be used to evaluate and characterize other solar technologies, including emerging technology such as perovskite as was shown in previous work (Ramirez et al., 2019; Velilla et al., 2019). Besides, in the case of unknown PRC, a full characterization of the device at outdoors can be done. In this sense, a CIGS module by Miasolé (Flex-02 120 N) was monitored for 600 h between 2019-05-09 and 2019-06-03, Fig. 9. Based on the PRC over time, the power delivery at STC was 113.06 ± 3.78 W, and at NOCT was 93.67 ± 4.20 W. While the Isc at STC was 4.53 ± 0.17 A and at NOTC was 3.75 ± 0.18 A (Fig. S7 and Table S7). These results suggested that the maximum power of device at STC was in the range of power defined by manufacturer (120 W), and that 200 h of exposure were enough to obtain that value. Crosslinking these results with the results showed in Fig. 7, it is possible to determine that between 200 and 600 h of exposure are enough to evaluate the outdoor performance of solar devices. Moreover, the behavior of the NOCT over time can be considered to evaluate the status of the devices according to the manufacturer data.
Fig. 8. Normalized average NOCT over time with respect to manufacturer datasheet. Dot lines indicated a tolerance of ± 5%.
amount of data recorded is statistically reliable and the maps represent the average performance during the exposure time. On the other hand, due to devices were not cleaned during the exposure, to observe the changes over time on the PRC suggested by IEC 61853-1 and taking into account the soiling and seasonal effects, the databases were processed by batches. Every batch included the measurements recorded during times period of 200 h. The data of every batch were processed based on the linear relationship between the electrical power and irradiance according to the linearity determination criteria. Then, the data of the batches were filtered considering just the irradiance levels defined by each PRC and a deviation of ± 5% (STC = 1000 W/m2, NOCT = 800 W/m2, LTC = 500 W/m2 and LIC = 200 W/m2). The results were shown on Fig. 7 through box plot to illustrate the mean and standard deviation of the measurements over time. Besides, due to commonly the manufacturers supplied the STC and NOCT on the datasheet (as the panels evaluated), but not other conditions, the average values of these two PRC over the exposure time were compared with the data supplied by manufacturers, Table 1.
4. Conclusions In this paper, a monitoring system conformed by electronic analyzers for measuring the I-V curve and a data management to store, synchronize and process the electrical and weather records were
Fig. 9. Outdoor evaluation of CIGS module. (a) Impact on the performance as a function of irradiance and ambient temperature (b) Power rating condition over time. The block colors are related to the PRC as follows: green to 1000 W/m2 (STC), blue to 800 W/m2 (NOCT), yellow to 500 W/m2 (LTI) and gray to 200 W/m2 (LIC). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 84
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developed. With this system, four commercial solar panels were evaluated and the average outdoor performance maps as a function of irradiance levels and device temperature were obtained. These maps allowed to visualize the most representative operative points and power rating conditions occurred during the exposure. On other hand, processing the data according to the irradiance levels defined by the power rating conditions suggests by IEC 61583, indicated that the NOCT is the most realistic conditions to evaluate the outdoor performance under the local weather conditions. Being this PRC the most suitable condition to check the devices status instead of the STC as suggested the international standard IEC 61853. Moreover, this condition could be used not only to define cleaning maintenance, but also, to identify the conditions to which the panels performed better. Being this information the input to select the panel or technology to be installed depending on the location irradiation levels. Finally, the behavior of PRC over time suggested that between 200 and 600 h of exposure are required to characterize the outdoor performance and obtain reliable PRC of solar devices.
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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement Esteban Velilla thank Colombia’s Administrative Department of Science Technology and Innovation (COLCIENCIAS), for the national doctoral scholarship number 727-2015 and contract number FP44842124-2017. The authors gratefully acknowledge the financial support provided by the Colombia Scientific Program within the framework of the call Ecosistema Científico (Contract No. FP44842- 218-2018). Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.solener.2019.10.051. References Agroui, K., 2012. Indoor and outdoor characterizations of photovoltaic module based on mulicrystalline solar cells. Energy Proc. 18, 857–866. https://doi.org/10.1016/j. egypro.2012.05.100. Cano, J.B., Valencia, J., Jaramillo, F., Velilla, E., 2015. Desarrollo e implementación de prototipo electrónico para la caracterización de paneles solares en condiciones de exteriors. Revista Politécnica 11, 41–50. Dash, P.K., Gupta, N.C., Rawat, R., Pant, P.C., 2017. A novel climate classification criterion based on the performance of solar photovoltaic technologies. Sol. Energy 144, 392–398. https://doi.org/10.1016/j.solener.2017.01.046. Duran, E., Piliougine, M., Sidrach-De-Cardona, M., Galan, J., Andujar, J.M., 2008. Different methods to obtain the I-V curve of PV modules: a review. In: Conf. Rec. IEEE Photovolt. Spec. Conf. https://doi.org/10.1109/PVSC.2008.4922578. Eltamaly, A.M., Farh, H.M.H., Othman, M.F., 2018. A novel evaluation index for the photovoltaic maximum power point tracker techniques. Sol. Energy 174, 940–956.
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Monitoring system to evaluate the outdoor performance of solar devices considering the power rating conditions
T
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Esteban Velillaa,b, Juan B. Canob, , Franklin Jaramilloa a b
Centro de Investigación, Innovación y Desarrollo de Materiales – CIDEMAT, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia Grupo en Manejo Eficiente de la Energía, GIMEL, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
A R T I C LE I N FO
A B S T R A C T
Keywords: Monitoring system Power rating conditions IEC 61853 Solar devices Outdoor performance
In order to evaluate the impact of irradiance and temperature on the outdoor performance of solar devices, a monitoring system including electronic analyzers for measuring the I-V curve and a data management to store, synchronize and process the electrical and weather records were developed. With this system, the average performance as a function of irradiance and temperature of commercial solar panels was obtained in natural sunlight without tracker considering up to 4700 h of exposure and sampling time of 1 min. Besides, the data were processed considering the irradiance levels defined by each power rating conditions suggested by IEC 61853. The results indicated that in tropical weather the Standard Test Conditions (STC) are not as representative to evaluate solar panels, due that an irradiance of 1000 W/m2 rarely occurs at 25 °C temperature of the panel. However, the Nominal Operative Cell Temperature (NOCT) was the most suitable condition to test outdoor performance according to the manufacturer data. Therefore, this condition could be considered to evaluate the status of devices and to define the maintenance guidelines of photovoltaic devices. Finally, the NOCT behavior over time suggested that between 200 and 600 h of exposure are more than enough to characterize and obtain reliable Power Rating Conditions (PRC) under the local conditions.
1. Introduction The performance of solar devices is generally specified at Standard Test Conditions (STC), conditions that rarely occurs in real operation (Dash et al., 2017; Virtuani et al., 2011). In this sense, international standards such as IEC 61853 (IEC 61853-1, 2011), suggests different Power Rating Conditions (PRC) as a function of irradiance and temperature for evaluating photovoltaic modules performance. Therefore, the power delivery of devices can be measured at indoors using a solar simulation and controlling the device temperature or at outdoors in natural sunlight. In the case of outdoor conditions, the time of exposure required to obtain the PRC is a big issue as some required conditions could never happen during the exposure. Moreover, transient effects related to unclear days, seasonal conditions and irradiance fluctuations caused mainly by clouds or shades, could affected the measurements. Besides, the performance depends on location, installation conditions, seasonal variations, shading and soiling effects, among other parameters (Phinikarides et al., 2014; Said et al., 2018). To track the power delivery from solar devices (panels and cells), electronic equipment such as inverters (Visa et al., 2016) and resistive load (Velilla et al., 2014) have been used. However, in these cases, the
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accuracy of the outdoor performance depend mainly on the maximum power point tracking technic implemented (Eltamaly et al., 2018). On other hand, measuring the Current vs Voltage curves (I-V) allowed to perform a full characterization of devices due that not only the maximum power can be extracted, but else, the short circuit current (Isc) and open circuit voltage (Voc). Moreover, these curves have been used to explain partial shading phenomenon and the implications on maximum power (Silvestre and Chouder, 2008), analyze solar cell degradation (Khenkin et al., 2019), estimate the parameters of electrical model (Velilla et al., 2018), among other. To measure the I-V curve of solar devices different hardware techniques have been used (Duran et al., 2008). For instance, a resistor is connected to the device under test (DUT), changing the resistance between a value near to zero (short circuit) and a higher value considered as open circuit (Nikoletatos and Halambalakis, 2018). It is to remark that additional hardware is required to change the resistance value such as potentiometer-motor systems, relay commuted series and parallel resistors arrays, digital potentiometers, among other (Van Dyk et al., 2005). Moreover, MOSFET transistors have been used as variable loads for I-V curve measurement allowing an easier control of the DUT operation point, without intervention of mechanical or electromechanical
Corresponding author. E-mail addresses: [email protected] (E. Velilla), [email protected] (J.B. Cano), [email protected] (F. Jaramillo).
https://doi.org/10.1016/j.solener.2019.10.051 Received 28 June 2019; Received in revised form 13 October 2019; Accepted 21 October 2019 0038-092X/ © 2019 International Solar Energy Society. Published by Elsevier Ltd. All rights reserved.
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operate for voltages up to 8 V and currents up to 3 A (solar cells and solar mini modules).
devices (Hassaine et al., 2014; Papageorgas et al., 2015; Willoughby et al., 2014; Willoughby and Osinowo, 2018). In both cases, heat dissipation is a concern, limiting the applicability at high power solar panels. Furthermore, DC/DC converters are used on I-V curve characterization (Enrique et al., 2005; Faifer et al., 2015), in this case a Pulse Width Modulation (PWM) signal controls the DUT operation point and allows to control the sweep over the I-V curve. However, the complexity and components number of this technique is increased compared to a resistive or transistor load technics. On the other hand, capacitive loads have also been reported to measure the I-V curve (Agroui, 2012; Cano et al., 2015). These types of devices work by connecting a discharged capacitor to the DUT and registering the voltage and current waveforms during the transient response. This technique has several advantages, the capacitor has no heat dissipation issues and it is possible to be used on high power applications. Moreover, the sweep over the curve is achieved by the capacitor dynamics, without using any external mechanism. However, it is not possible to control the direction (from short circuit to open circuit) and speed of the sweep, because it depends on capacitance and DUT characteristics. Finally, it is also possible to use a controlled voltage source to trace I-V curve (Duran et al., 2008), this allows a more complete characterization, allowing to explore the reverse bias region, voltages higher than the open circuit voltage. Also, it is possible to directly control the voltage change rate (V/s rate) and control the sweep direction making it practical for hysteresis measurements (Li et al., 2017). However, this technique involves an additional power supply increasing costs and system complexity. Taking into account the advantages and disadvantages of the aforementioned techniques, and considering that the I-V curve of solar devices depends on the weather variables such as temperature and irradiance, which are not constant and could change abruptly at outdoor conditions by clouds, shades, drops, etc., we implemented a monitoring system that included electronic devices based on portability, low cost and faster response to measure the I-V curve according to the electrical power of the device evaluated and a data management to store, synchronize and process the electrical and atmospheric data. Finally, the data were processed to obtain the impact of irradiance and temperature on the performance and the power rating conditions defined by the standard IEC 61853.
2.1.1. Solar panel analyzer (SPA) Fig. 1a shows the building blocks of device that involved a panel, a capacitive load, solid state relays for charging and discharging control, voltage sensor (AMC1200-Isolation amplifier, Texas Instruments), current sensor (ACS-711 series - Hall effect, Allegro), an analog to digital converter (ADC) and a microcontroller. The control and communication systems were implemented on a 32 bits microcontroller (PIC32MX230F064D, Microchip). For safety reasons, due to voltage and current levels of panel, the isolation between power (load, sensors, relays) and control circuit sections (microcontroller, communications) was implemented, Fig. 1b. Voltage and current signals are digitalized by a sigma-delta analog to digital converter (AD7172-2, Analog devices). Serial port interface communication was implemented between converter and microcontroller. This setup allows sampling times of 0.32 ms for each I-V curve point (total time to sequentially take a voltage sample and a current sample). Sampling time is important to determine the minimum capacitance value required to measure the charging transient. A serial port emulation protocol was implemented over USB, providing a command set to control analyzer functions and retrieve data. Finally, the expansion sensors block allows direct connection to the analog-digital converter of microcontroller, intended for external sensor interface such as irradiance and temperature variables. The SPA was programmed as follow. First, capacitor is discharged by means of a resistor in parallel connected by a solid-state relay. Then, solar panel is keep at open circuit. Second, the Voc of panel is measured. Third, solar panel is connected to capacitor and voltage and current sampling is started. Fourth, the analyzer keeps sampling while a preconfigured time is over. Fifth, the SPA is disconnected from load. Sixth, the capacitor is discharged. Seventh, I-V data is printed via USB port.
2.1.2. Solar cell analyzer (SCA) This analyzer used a controlled voltage supply (potentiostat) to set the voltage on the DUT. The voltage is changed in the interest range of the I-V curve and is digitally controlled through a digital to analog converter, Fig. 2a. The potentiostat circuit was implemented using linear circuits (operational amplifiers), due to the low voltage, current and power of the solar cells, Fig. 2a. Comparing with a switching converter, this implementation has more power losses but is simpler and quicker to implement. Operational amplifiers series OPA548T (Texas Instruments) where selected due to their output current characteristics. A trans-resistance amplifier configuration is used as current sensor considering the same operational amplifiers. A differential amplifier (LT1167, Linear Technologies) was used as voltage sensor, Fig. 2b. Notice that this prototype does not have any isolation between cell and control circuits. A digital to analog converter (TLV5638, Texas Instruments) was used for generation of the voltage references for solar cell. Signal digitalization, processing, sensor expansion and communications were carried out using the same components described for panel analyzer. The SPC was programmed as follows. First, the DUT is disconnected from the analyzer and Voc is measured. Second, the start voltage is configured on the DAC and the solar device is connected to register the voltage and current. Third, the DAC voltage is linearly increased or decreased, and measurements are registered. Four, the process continues until reach the end voltage. Fifth, the DUT is disconnected from analyzer. Sixth, current and voltage data are sent via USB port. Other information related to the solar analyzer is shown in supporting information, Tables S1 and S2.
2. Methodology With the aim to obtain the outdoor performance of solar devices, a monitoring system that included the solar analyzers and the data management was developed. Hence, two different I-V tracing devices focused on in-situ outdoor measurement capabilities such as low cost, portability, faster response and scalability (Section 2.1), and a weather system for measuring the irradiance levels and temperatures were implemented (Section 2.2.2). Moreover, a data management system to store and process the electrical (I-V curve) and weather data was developed. The data management included processing algorithms in order to minimize the effects related to unclear days, short term irradiance fluctuations or atypical data and obtain the outdoor performance as function of irradiance levels and temperature. Finally, the system was tested by measuring different commercial solar devices and validated according to manufacturer datasheet. 2.1. Solar analyzer (SA) Two different types of I-V curve tracers (SA) were implemented. One used the capacitive load technique due to simplicity, power dissipation and cost concerns. This prototype is intended for DUTs that operate for voltages up to 250 V and currents up to 12.5 A (solar panels, Fig. 1). Other used a four quadrant DC supply due to greater flexibility on sweep direction and speed. This prototype is intended for DUT that 80
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Fig. 1. Panel analyzer. (a) Block diagram. (b) Printed Circuit Board.
2.2. Data management 2.2.1. Electrical performance measurement To measure the I-V curve of solar devices, the Solar Analyzer (SA) included an embedded computer (The Raspberry Pi 3) connected to each SPA or SCA, Fig. 3. Raspberry Pi runs Raspbian OS and executes the following tasks:
• System date and time are keeping updated using the Network Time • • •
Protocol Daemon (NTPD). It guarantees low error on measurement time stamps. An SSH (Secure Shell) interface allows remote access for modification of system configuration settings (as measurement frequency, sensor calibration, etc) or scripts edition. Cron service (Linux task programmer) is configured to execute every minute the Python script to communicate the Raspberry with the SPA or SCA and start the measurement process. The registers (I-V data) are saved in a CSV file including the timestamp. Finally, CSV files are synchronized every hour with the remote server, using the Rsync service over SSH, Fig. 4.
Fig. 3. Final SA for on-field installation.
It is to remark that before any on-field testing, current and voltage sensors are calibrated to guarantee measurement accuracy of SA.
Sensor data were collected using a Sensor Box Datalogger (FRONIUS International GMBH). Sensor data were sent via Ethernet communication to the Raspberries for local storage on CSV files (adding the timestamp). Finally, Raspberry synchronized the records related to irradiance levels and temperatures every hour with the remote server using Rsync service over SSH. As mentioned before, electrical (I-V) and weather (irradiance and temperatures) data are uploaded to the remote server, not only to
2.2.2. Weather data measurement A complete characterization of solar devices requires a weather monitoring system. Thus, solar irradiance and temperature sensors where implemented. Solar irradiance was measured using the Spektron 210 (TRITEC international) sensor. Ambient and panel/cell temperatures were measured using PT-1000 sensors (TRITEC international).
Fig. 2. Solar cells analyzer. (a) Block diagram. (b) Printed Circuit Board. 81
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Fig. 4. Monitoring system to evaluate outdoor performance.
provide backup, but also to centralize monitoring and processing operations, Fig. 4. Notice that the system can be easily scaled to include more SA.
2.2.3. Data processing From the electrical data stored in the server and measured through the I-V curve by the SA, the short circuit current (Isc), open circuit voltage (Voc) and the maximum power (Pmax) of devices were extracted. Then, a Python script is executed to link the electrical data with the irradiance levels and temperatures records using the captured timestamps, then a database by device is created (see Fig. S1). After that, the databases are processed based on the linear relationship between the electrical power and irradiance according to the linearity determination criteria, in order to minimize the effects related to unclear days, short term irradiance fluctuations or atypical data (Velilla et al., 2019), Fig. 5. According to IEC 61853-1, different PRC such as Standard Test Condition (STC, measure at 1000 W/m2 of irradiance and 25 °C of cell temperature), Nominal Operating Cell Temperature (NOCT, measure at 800 W/m2 of irradiance and 20 °C of ambient temperature), Low Irradiance Condition (LIC, measured at 200 W/m2 of irradiance and 25 °C of cell temperature), High Temperature Condition (HTC, measure at 1000 W/m2 of irradiance and 75 °C of cell temperature) and Low Temperature Condition (LTC, measure at 500 W/m2 of irradiance and 15 °C of cell temperature) have to be measured during the evaluation time or extrapolated to determine the impact of irradiance and temperature on the performance. However, this impact could be graphically observed through maps, including the most representatives operative points measured during the exposure time and the PRCs. Additionally, the data could be processed to perform different temporary analyzes, such as the power delivery over time (Ramirez et al., 2019).
Fig. 5. Linear relationship between maximum power and irradiance of Sharp panel. The gray dots indicated the measurements registered during the exposure and the linear polynomial shown in the legend corresponded to the fitted data. On the top is included the coefficient of determination of the fitted process, suggesting a linear relationship between both variables. The dots close to the fitted line corresponded to the filtered data.
3. Results To evaluate the outdoor performance of solar devices, the SA were linked with the monitoring system that included the data management implemented. Then, three commercial solar devices were monitored 82
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Fig. 6. Power delivery map as a function of irradiance and panel temperature. (a) Sharp NU-RC290 monitored between 2018-07-05 and 2019-01-20, (b) Yingli YL275D-30b monitored between 2018-11-22 and 2019-04-24, and (c) HIT VBHN330SJ47 monitored between 2019-02-23 and 2019-06-03. The maximum value of power delivery is shown on top of each map, and the colorbar indicates the values of Pmax.
Fig. 7. Power rating conditions over time for evaluated panels. Data for: (a) Sharp, (b) Yingli and (c) HIT. The block colors are related to the PRC as follows: green to 1000 W/m2 (STC), blue to 800 W/m2 (NOCT), yellow to 500 W/m2 (LTI) and gray to 200 W/m2 (LIC). Table 1 STC and NOCT conditions for solar devices supplied by manufacturer and obtained during the outdoor test. Devices
Sharp NU-RC290
Yingli YL275D-30b
HIT VBHN330SJ47
Variables
Pmax (W) Isc (A) Voc (V) Pmax (W) Isc (A) Voc (V) Pmax (W) Isc (A) Voc (V)
STC (1000 W/m2)
NOCT (800 W/m2)
Data sheet
Mean
std
Error (%)
Data sheet
Mean
std
Error (%)
290 9.8 39.3 275 9.34 38.9 330 6.07 69.7
257.33 10.26 35.67 243.12 9.62 34.15 315.82 6.56 64.35
8.31 0.39 0.50 8.34 0.37 0.58 9.44 0.26 0.80
11.26 −4.69 9.23 11.59 −2.99 12.21 4.29 −8.07 −0.51
212 7.93 36.2 200.6 7.55 35.90 247.2 4.91 65.1
212.78 8.16 35.78 200.56 7.78 34.18 253.61 5.15 64.76
6.78 0.38 0.44 7.03 0.33 0.51 9.03 0.25 0.63
−0.36 −2.34 1.16 0.02 −3.04 4.79 −2.59 −4.89 0.52
performance maps as a function of the irradiance and temperature of devices considering the entire exposure time and processing the data with the linearity determination criteria. In spite of the maps of power delivery allowed us to graphically visualize the impact of irradiance and temperature on the devices performance, two main points have to be remarked. First, this performance is related to the most frequency conditions happened during the exposure time. Second, due to the setup was carried out on tropical weather conditions, it is to expect that some PRC such as STC, HTC and LTC rarely occur. Therefore, some operative points or PRC could not be included in the maps due that these could not be considered as statistically representative. Nevertheless, due to the exposure time exceeding 2600 h and the sampling time between measurements was 1 min, the
between august of 2018 and June of 2019 considering different period of time and natural sunlight without tracker on the terrace of the University Research Center - SIU from the Universidad de Antioquia (6° 15′ 38″ N 75° 34′ 05″W), facing south at fixed angle of 13° (see Fig. S2). Hence, the monitoring system was setup to record every minute during the light-hours the electrical variables (I-V curves) and weather data (irradiance levels and temperatures). Moreover, the files with electrical and weather data were synchronized every 24 h to back-up the records in the server and update the databases of devices evaluated. After that, the databases can be processed at any time considering the linearity determination criteria between the irradiance and power to minimize transient effects related to irradiance fluctuations or atypical data (Fig. 5 and Figs. S3-6). For instance, in Fig. 6 are showed the 83
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Based on the errors on Table 1, it is remarked that the NOCT was the most suitable power rating condition that represent the outdoor performance, being the error within the range ± 5% for the variables analyzed (see Table 1 and Tables S3-5). Moreover, normalizing the NOCT mean values with respect to the manufacturer data (Fig. 8), it was observed that the most part of exposure time the devices were within the range of ± 5%. Therefore, the evaluation of the NOCT at outdoor conditions instead of the STC as suggested the IEC 61-583-1, it could be used not only to check the status of devices with respect to manufacturer data, but also, to define cleaning maintenance and to identify the most suitable conditions of the panels. In this last case, normalizing the PRC with respect to the NOCT supplied by manufacturer (Table S6), it was possible to identify that the HIT panels performed better at high irradiance levels, while other panels worked better at lower irradiances. From this way, a most trustfully comparison between panels at outdoor conditions can be performed indeed to select the panel or technology that could be installed depending on the most representative irradiance levels of the location. Finally, the monitoring system could be used to evaluate and characterize other solar technologies, including emerging technology such as perovskite as was shown in previous work (Ramirez et al., 2019; Velilla et al., 2019). Besides, in the case of unknown PRC, a full characterization of the device at outdoors can be done. In this sense, a CIGS module by Miasolé (Flex-02 120 N) was monitored for 600 h between 2019-05-09 and 2019-06-03, Fig. 9. Based on the PRC over time, the power delivery at STC was 113.06 ± 3.78 W, and at NOCT was 93.67 ± 4.20 W. While the Isc at STC was 4.53 ± 0.17 A and at NOTC was 3.75 ± 0.18 A (Fig. S7 and Table S7). These results suggested that the maximum power of device at STC was in the range of power defined by manufacturer (120 W), and that 200 h of exposure were enough to obtain that value. Crosslinking these results with the results showed in Fig. 7, it is possible to determine that between 200 and 600 h of exposure are enough to evaluate the outdoor performance of solar devices. Moreover, the behavior of the NOCT over time can be considered to evaluate the status of the devices according to the manufacturer data.
Fig. 8. Normalized average NOCT over time with respect to manufacturer datasheet. Dot lines indicated a tolerance of ± 5%.
amount of data recorded is statistically reliable and the maps represent the average performance during the exposure time. On the other hand, due to devices were not cleaned during the exposure, to observe the changes over time on the PRC suggested by IEC 61853-1 and taking into account the soiling and seasonal effects, the databases were processed by batches. Every batch included the measurements recorded during times period of 200 h. The data of every batch were processed based on the linear relationship between the electrical power and irradiance according to the linearity determination criteria. Then, the data of the batches were filtered considering just the irradiance levels defined by each PRC and a deviation of ± 5% (STC = 1000 W/m2, NOCT = 800 W/m2, LTC = 500 W/m2 and LIC = 200 W/m2). The results were shown on Fig. 7 through box plot to illustrate the mean and standard deviation of the measurements over time. Besides, due to commonly the manufacturers supplied the STC and NOCT on the datasheet (as the panels evaluated), but not other conditions, the average values of these two PRC over the exposure time were compared with the data supplied by manufacturers, Table 1.
4. Conclusions In this paper, a monitoring system conformed by electronic analyzers for measuring the I-V curve and a data management to store, synchronize and process the electrical and weather records were
Fig. 9. Outdoor evaluation of CIGS module. (a) Impact on the performance as a function of irradiance and ambient temperature (b) Power rating condition over time. The block colors are related to the PRC as follows: green to 1000 W/m2 (STC), blue to 800 W/m2 (NOCT), yellow to 500 W/m2 (LTI) and gray to 200 W/m2 (LIC). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 84
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developed. With this system, four commercial solar panels were evaluated and the average outdoor performance maps as a function of irradiance levels and device temperature were obtained. These maps allowed to visualize the most representative operative points and power rating conditions occurred during the exposure. On other hand, processing the data according to the irradiance levels defined by the power rating conditions suggests by IEC 61583, indicated that the NOCT is the most realistic conditions to evaluate the outdoor performance under the local weather conditions. Being this PRC the most suitable condition to check the devices status instead of the STC as suggested the international standard IEC 61853. Moreover, this condition could be used not only to define cleaning maintenance, but also, to identify the conditions to which the panels performed better. Being this information the input to select the panel or technology to be installed depending on the location irradiation levels. Finally, the behavior of PRC over time suggested that between 200 and 600 h of exposure are required to characterize the outdoor performance and obtain reliable PRC of solar devices.
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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement Esteban Velilla thank Colombia’s Administrative Department of Science Technology and Innovation (COLCIENCIAS), for the national doctoral scholarship number 727-2015 and contract number FP44842124-2017. The authors gratefully acknowledge the financial support provided by the Colombia Scientific Program within the framework of the call Ecosistema Científico (Contract No. FP44842- 218-2018). Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.solener.2019.10.051. References Agroui, K., 2012. Indoor and outdoor characterizations of photovoltaic module based on mulicrystalline solar cells. Energy Proc. 18, 857–866. https://doi.org/10.1016/j. egypro.2012.05.100. Cano, J.B., Valencia, J., Jaramillo, F., Velilla, E., 2015. Desarrollo e implementación de prototipo electrónico para la caracterización de paneles solares en condiciones de exteriors. Revista Politécnica 11, 41–50. Dash, P.K., Gupta, N.C., Rawat, R., Pant, P.C., 2017. A novel climate classification criterion based on the performance of solar photovoltaic technologies. Sol. Energy 144, 392–398. https://doi.org/10.1016/j.solener.2017.01.046. Duran, E., Piliougine, M., Sidrach-De-Cardona, M., Galan, J., Andujar, J.M., 2008. Different methods to obtain the I-V curve of PV modules: a review. In: Conf. Rec. IEEE Photovolt. Spec. Conf. https://doi.org/10.1109/PVSC.2008.4922578. Eltamaly, A.M., Farh, H.M.H., Othman, M.F., 2018. A novel evaluation index for the photovoltaic maximum power point tracker techniques. Sol. Energy 174, 940–956.
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