Perforated tubes were used to create a film of water across the front surface of the PV panels at a low and high flow rate of 1.317 L/min and 2.625 L/min, respectively

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Philippine e-Journal for Applied Research and Development 13(2023), 44-54 ISSN 2449-3694 (Online) https://pejard.slu.edu.ph/vol.13/2023.07.29.pdf

Testing the Effect of Active Water Cooling using Perforated Tubes on the Power Generation of a 5-Watt Polycrystalline Solar

Photovoltaic Panel

Thor Julicer Alvis^*1 and Gabriela Monica Gonzales¹

1Department of Electrical Engineering, College of Engineering and Agro-Industrial Technology, University of the Philippines Los Baños

* Corresponding author ([email protected])

Received, 17 January 2023; Accepted, 14 July 2023; Published, 29 July 2023

Copyright @ 2023 T.J. Alvis, and G.M. Gonzales. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

One challenge in solar photovoltaic (PV) power generation is its low output due to high panel temperature. To address this, cooling systems are implemented to dissipate heat in panels. This study investigates the effect of an active cooling system on the power generation of a 5-watt polycrystalline PV panel. Perforated tubes were used to create a film of water across the front surface of the PV panels at a low and high flow rate of 1.317 L/min and 2.625 L/min, respectively.

Results of the low flow rate reveal a temperature difference of 7.52℃, a power increase of 8.80%

and a total energy yield of 9.71% For the high flow rate, the temperature is lowered by 7.75℃, while power and energy gained by 12.22% and 11.79%, respectively. Results show a substantial increase in power and energy output of PV panels due to the addition of an active cooling system.

Thus, the cooling method implemented is a viable way to improve PV panel operation.

Key Words: Renewable energy sources, renewable energy technologies, panel performance, flow rate, module temperature

Philippine e-Journal for Applied Research and Development

Website: pejard.slu.edu.ph ISSN 2449-3694 (Online)

Introduction

Photovoltaic (PV) systems utilize light energy to generate electrical energy (Richardson, 2020). While an increase in light intensity or solar irradiance increases the energy output, it also results in an increase in the solar panel temperature, affecting its efficiency (Dwivedi et al., 2020). As a solution, cooling systems are incorporated in PV systems to dissipate heat and improve PV performance.

Active cooling systems utilize a coolant circulated by a fan or pump to lower PV panel temperatures (Dwivedi et al., 2020). Various studies explored the effect of water flow rate as a cooling system in solar PV performance. Luboń

et al. (2020) poured tap water to create a water film over a 240-Watt polycrystalline PV panel at a fixed flow rate. A temperature difference of 20℃ between the cooled and uncooled panel was recorded, and the power generated increased by 22%. The study also showed that the voltage increased significantly in the cooled panel. Al- Wajih et al. (2020) also employed a front surface water cooling on a 100-Watt PV panel using a pump connected to a sprinkler. Their study saw a temperature difference of 20℃ and a 30%

increase in power. Gaur and Tiwari (2014) also tested a fixed flow rate cooling system on an amorphous silicon PV panel through perforated tubes. A 23℃ temperature difference and about a 7% increase in power efficiency were recorded.

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Philippine e-Journal for Applied Research and Development 13(2023), 44-54Website: pejard.slu.edu.ph ISSN 2449-3694 (Online)

Voltage values also improved in the cooled panels. Iqbal et al. (2016) also tested the use of water canes on a 12-Watt polycrystalline panel and found a 6.4% increase in power. Valenzuela et. al (2020) tested the effect of a hybrid cooling system using air and water on a 150-Watt panel. Fans were installed at the rear of the panel while water nuzzles connected to a pump were installed in the front part. Panel efficiency increased by 22.5%, which is attributed to high current values. Studies on multiple flow rates were also conducted. The study of Santos et al.

(2017) showed that using a pump to generate four different flow rates resulted to an increase in power on a 265-Watt panel by about 9.5 to 12%.

It also showed that the higher the flow rate, the higher the gains in power. Ahmed and Danook (2018) tested three flow rates through perforated tubes on 150-Watt monocrystalline panels and saw a 3 to 7% increase in power generation, but with an inverse relationship of flow rate to power with the lowest flow rate having the highest increase in power output.

General themes can be observed from these studies. One is that active cooling systems effectively lower panel temperatures. Two is that the cooling method improve power generation in solar PVs. Some points can also be further investigated. Some studies show that increasing the flow rate resulted to less power output (Ahmed and Danook, 2018), while some studies show that it resulted to an increase (Santos et al., 2017). Thus, it still poses the question on the effect of the flow rate on the power generation of PV panels. Some studies also highlighted that voltage contributes the most to the increase in power output (Lubon et al., 2020, and Gaur and Tiwari, 2014), while some claim it is current (Valenzuela et al., 2020). On the other hand, other studies did not investigate which parameter contributes more in power generation (Al-Wajih et al.,2020, Santos et al., 2017, and Iqbal et al., 2016). Measuring these parameters may also bridge the gap in these studies. All these studies also only conducted their testing from a few hours up to a few days. This setup may not capture the overall effect of the cooling system when the panel is exposed to actual daily weather conditions.

Thus, the study aims to investigate the effect of an active cooling system to the power

generation of a 5-watt solar panel where a thin film of water will be continuously applied across its top surface for 21 days. Specifically, it aims to 1) measure the voltage, current, and temperature of a non-cooled and two active water-cooled solar PV systems; 2) compare the change in power and energy generated by the solar PV systems at different flow rates; and 3) determine which flow rate is more effective in increasing the power output of the solar PV system. The results of this study also aim to show how active water cooling can be applied in existing solar PV systems and possibly become a viable option to improve the power generation of these systems.

Methodology

The materials used in the study were based on the paper of El Hammoumi et al. (2018), which implemented an Arduino based data logging system. Various papers such as those by Sandhya et al. (2015), Santos et al. (2017) and Al-Wajih et al. (2020) proved to be valuable references in designing the water cooling system and testing stand, respectively.

Software Used

The software programs used in the study are as follows:

 Arduino IDE – an open-source software used to interface a computer to any Arduino board, allowing for writing and uploading of code to a given board when connected

 CoolTerm – allows for automated data collection and recording of serial data collected by an Arduino

 Microsoft Excel – allows for compilation of data collected in the study and efficient calculation of parameters such as the power output and Pearson’s correlation coefficient.

Materials Used

Table 1 presents the various materials used throughout the study and their respective specifications.

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46 T.J. Alvis, and G.M. Gonzales

Research Proper

An overview of the general process of the study is presented in the form of a flowchart in Figure 1.

This covers procurement and fabrication, to data collection, and finally interpretation of results.

Experimental Setup

Figure 2 presents both the schematic diagram and actual setup of the testing stand in the experiment. The control, low flow rate, and high flow rate cases were done simultaneously

to ensure fair comparison between each case. An inclination of 22º from the horizontal while facing south was implemented to ensure optimum efficiency of the solar PV panels for the testing period, as this was found to be the mean ideal tilt for the local area during the timeframe of the experiment according to the models provided by Malicdem (2015) and Solargis (2021). Partial shading was eliminated by locating the testing stand in an open area. Active water cooling was achieved using DC water pumps placed in the water container. Constant flow rates for these were attained by having them operate in parallel, Table 1. Materials used in the study.

Material Specifications

Parameter Value

RS Pro 12V 5Wp Solar Panel (9046128)

Output (Wp) NOCT (ºC) V_OC(V) I_SC(A) V_MP(V) I_MP(A)

Current Temperature Coefficient (A/K) Voltage Temperature Coefficient (V/K) Power Temperature Coefficient (W/K)

5 48 21 0.39 16.8 0.3 + 0.003

– 0.13 – 0.675 Arduino Uno R3

Microcontroller Digital I/O Pins Analog Input Pins

Atmega328p 14

6 F031-06 Voltage Sensor

Module

Input Voltage Range (V) Voltage Detection Range (V) Analog Voltage Resolution (V)

0 to 25 0.02445 to 25

0.00489 INA169 Current Sensor

Module

Input Voltage Range (V) Maximum Input Current (A) Analog Current Resolution (A)

2.7 to 60 5 0.00489 DS18B20 Temperature

Sensor Range of Measurements (ºC)

Accuracy from – 10 to + 85ºC (ºC)

– 55 to + 125

± 0.5 Osram 12V 5W Bulb

(6418ULT)

Nominal Wattage (W) Nominal Voltage (V) Resistance (𝛀)

5 12 28.8

Immersible DC Water Pump (FIT0200)

Input Voltage (V) Input Current (mA) Pumping Head (cm) Flow Rate (L/hr)

3.5 to 12 65 to 500 40 to 220 100 to 350

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102 Figure 1. Flowchart of the research.

103

Figure 1. Flowchart of the research.

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both in wiring and in output. Reliable operation for both the Arduino and DC water pumps was ensured by an external power source.

The corresponding circuit diagram of the experiment is shown in the left portion of Figure 3, presenting each test case, how each sensor is placed, and its respective pinout connection to the Arduino. An image of the actual wiring implemented is then seen in the right portion of the aforementioned figure. To ensure maximum power output of the solar PV panel, a load with ideal resistance must be used which was calculated to be 56 Ω. In order to match this, two Osram 6418ULT bulbs connected in series were used as the load, providing 57.6 Ω of resistance, which was approximately that of the computed ideal load for the given PV panel.

A close-up view of the three test cases of the experiment is given in Figure 4. A single

pump was used in the low flow rate case, with two pumps being used in the high flow rate case, resulting in flow rates of 1.317 L/min and 2.625 L/

min, respectively. Also seen is the use of flexible PVC hoses to disperse the water evenly in a thin film across the entire front panel surface.

Arduino Data Collection

Each of the analog sensors used in the experiment sends an output signal between 0 and 5 V. However, once received by the Arduino’s analog pins, this reading is converted into a number between 0 and 1023, and this value is then sent to the connected computer. The setup done in this study allowed for the Arduino to collect data points every 1.5 seconds. Therefore, to have a usable data set, these analog values need to be converted to digital values. This can

104 Figure 2 presents both the schematic diagram and actual setup of the testing stand in the 105 experiment. The control, low flow rate, and high flow rate cases were done simultaneously to 106 ensure fair comparison between each case. An inclination of 22º from the horizontal while facing 107 south was implemented to ensure optimum efficiency of the solar PV panels for the testing period, 108 as this was found to be the mean ideal tilt for the local area during the timeframe of the experiment 109 according to the models provided by Malicdem (2015) and Solargis (2021). Partial shading was 110 eliminated by locating the testing stand in an open area. Active water cooling was achieved using 111 DC water pumps placed in the water container. Constant flow rates for these were attained by 112 having them operate in parallel, both in wiring and in output. Reliable operation for both the 113 Arduino and DC water pumps was ensured by an external power source.

114 115

116 Figure 2. (a) Schematic diagram; and (b) actual image of the testing stand.

117 118

The corresponding circuit diagram of the experiment is shown in the left portion of Figure 119 3, presenting each test case, how each sensor is placed, and its respective pinout connection to the 120 Arduino. An image of the actual wiring implemented is then seen in the right portion of the 121 aforementioned figure. To ensure maximum power output of the solar PV panel, a load with ideal 122 resistance must be used which was calculated to be 56 Ω. In order to match this, two Osram 123 6418ULT bulbs connected in series were used as the load, providing 57.6 Ω of resistance, which 124 was approximately that of the computed ideal load for the given PV panel.

125 126

127 Figure 3. (a) Circuit diagram; and (b) actual image of data collection system.

128

Figure 2. (a) Schematic diagram; and (b) actual image of the testing stand.

104 Figure 2 presents both the schematic diagram and actual setup of the testing stand in the 105 experiment. The control, low flow rate, and high flow rate cases were done simultaneously to 106 ensure fair comparison between each case. An inclination of 22º from the horizontal while facing 107 south was implemented to ensure optimum efficiency of the solar PV panels for the testing period, 108 as this was found to be the mean ideal tilt for the local area during the timeframe of the experiment 109 according to the models provided by Malicdem (2015) and Solargis (2021). Partial shading was 110 eliminated by locating the testing stand in an open area. Active water cooling was achieved using 111 DC water pumps placed in the water container. Constant flow rates for these were attained by 112 having them operate in parallel, both in wiring and in output. Reliable operation for both the 113 Arduino and DC water pumps was ensured by an external power source.

114 115

116 Figure 2. (a) Schematic diagram; and (b) actual image of the testing stand.

117 118

The corresponding circuit diagram of the experiment is shown in the left portion of Figure 119 3, presenting each test case, how each sensor is placed, and its respective pinout connection to the 120 Arduino. An image of the actual wiring implemented is then seen in the right portion of the 121 aforementioned figure. To ensure maximum power output of the solar PV panel, a load with ideal 122 resistance must be used which was calculated to be 56 Ω. In order to match this, two Osram 123 6418ULT bulbs connected in series were used as the load, providing 57.6 Ω of resistance, which 124 was approximately that of the computed ideal load for the given PV panel.

125 126

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49

vary between sensors, and will later be discussed in further detail. A general overview of this conversion process is shown in Figure 5.

Measurement of Variables

Voltage. The voltage output of each panel was measured using F031-06 voltage sensor modules.

Each solar PV panel had a corresponding sensor placed parallel to its load. As it is an analog sensor, conversion of its output back to the measured value across its terminals is given by the following equation (El Hammoumi et al., 2018):

A close-up view of the three test cases of the experiment is given in Figure 4. A single 129 pump was used in the low flow rate case, with two pumps being used in the high flow rate case, 130 resulting in flow rates of 1.317 L/min and 2.625 L/min, respectively. Also seen is the use of flexible 131 PVC hoses to disperse the water evenly in a thin film across the entire front panel surface.

132 133

134 Figure 4. Close up view of the experiment’s test cases.

135 136

137 Each of the analog sensors used in the experiment sends an output signal between 0 and 5 138 V. However, once received by the Arduino’s analog pins, this reading is converted into a number 139 between 0 and 1023, and this value is then sent to the connected computer. The setup done in this 140 study allowed for the Arduino to collect data points every 1.5 seconds. Therefore, to have a usable 141 data set, these analog values need to be converted to digital values. This can vary between sensors, 142 and will later be discussed in further detail. A general overview of this conversion process is shown 143 in Figure 5.

144 145

146 147

Figure 5. Data collection process from analog sensors.

148 149

150 Voltage. The voltage output of each panel was measured using F031-06 voltage sensor 151 modules. Each solar PV panel had a corresponding sensor placed parallel to its load. As it is an 152 analog sensor, conversion of its output back to the measured value across its terminals is given by 153 the following equation (El Hammoumi et al., 2018):

154

𝑉𝑉 = 𝑉𝑉_{𝑜𝑜𝑜𝑜𝑜𝑜,1} ×₁₀₂₃²⁵ (Equation 1) 155

156 Where is the voltage across the terminals of the sensor measured in volts, and 𝑉𝑉_{𝑜𝑜𝑜𝑜𝑜𝑜,1}is 157 the analog output from the Arduino ranging from 0 to 1023. Each of the three sensors used were 158 connected to the Arduino, referring to the different scenarios tested: control, low flow rate, and 159 high flow rate.

160

(Equation 1) Where V is the voltage across the terminals of the sensor measured in volts, and V_out,1 is the analog output from the Arduino ranging from 0 to 1023. Each of the three sensors used were connected to the Arduino, referring to the different scenarios tested: control, low flow rate, and high flow rate.

Current. INA169 current sensor modules were used to measure the current produced by each panel. Each solar PV panel had one of these sensors placed in series before the load and voltage sensor. Being an analog sensor, the following equation reconverts the output into a useable value (El Hammoumi et al., 2018):

161 Current. INA169 current sensor modules were used to measure the current produced by 162 each panel. Each solar PV panel had one of these sensors placed in series before the load and 163 voltage sensor. Being an analog sensor, the following equation reconverts the output into a useable 164 value (El Hammoumi et al., 2018):

165

𝐼𝐼 = 𝑉𝑉𝑜𝑜𝑜𝑜𝑜𝑜,2 ×₁₀₂₃²⁵ (Equation 2) 166

167 Where is the current through the terminals of the sensor measured in amperes, and

168 𝑉𝑉_{𝑜𝑜𝑜𝑜𝑜𝑜,2} is the analog output from the Arduino ranging from 0 to 1023. The outputs from these

169 sensors were connected to the Arduino similar to that of the voltage sensor.

170

171 Temperature. DS18B20 sensors were used to measure the rear surface temperatures of each 172 PV panel, water temperature, and ambient temperature throughout the experiment. The sensors 173 that measured the rear panel temperature were attached via thermal paste, which ensured maximum 174 conduction between the panel surface and sensor.

175 Unlike the voltage and current sensors, no additional computation was required to retrieve 176 the value of the temperature reading from the sensor, however, additional libraries needed to be 177 enabled in the Arduino IDE, namely: the Dallas Temperature library and the One Wire library.

178 Once these were installed, the data received by the computer served as the recorded temperature 179 in Celsius.

180 Flow Rate. The volumetric flow rate of water used in the experiment was measured by 181 collecting the amount of water released by each of the cooling systems over a span of one minute 182 as shown in the following equation:

183

𝑉𝑉̇ = ^{𝑑𝑑𝑑𝑑}_{𝑑𝑑𝑜𝑜} (Equation 3) 184

185 Where is the volumetric flow rate in L/min, is the change in volume in liters, and

186 is the change in time in minutes.

187 188

Power Generated

189 To ensure accuracy of the data, power is computed per data point, which is collected every 190 1.5 seconds, and not from the averaged-out values of voltage and current, this parameter was 191 computed by utilizing the following equation:

192 𝑃𝑃 = 𝑉𝑉𝐼𝐼 (Equation 4)

193 194

Where is the computed power in watts, is the measured voltage in volts, and is the 195 measured current in amperes.

196 197

Energy Generated

198 The total energy generated is taken by getting the power generated and multiplying this to 199 the time this is supplied, as shown in the following equation:

200 𝐸𝐸 = 𝑃𝑃𝑃𝑃 (Equation 5)

201 202

Where E is the total energy generated per given time in watt-hours, P is the computed 203 power in watts, and t is the time that power is supplied.

204 205

(Equation 2) Where I is the current through the terminals of the sensor measured in amperes, and V_out,2 is the analog output from the Arduino ranging from 0 to 1023. The outputs from these sensors were connected to the Arduino similar to that of the voltage sensor.

Temperature. DS18B20 sensors were used to measure the rear surface temperatures of each PV panel, water temperature, and ambient temperature throughout the experiment.

The sensors that measured the rear panel temperature were attached via thermal paste, which ensured maximum conduction between the panel surface and sensor.

132 133

135 136

144 145

146 147

148 149

154

𝑉𝑉 = 𝑉𝑉_{𝑜𝑜𝑜𝑜𝑜𝑜,1} ×₁₀₂₃²⁵ (Equation 1) 155

156 Where is the voltage across the terminals of the sensor measured in volts, and 𝑉𝑉_{𝑜𝑜𝑜𝑜𝑜𝑜,1}is 157 the analog output from the Arduino ranging from 0 to 1023. Each of the three sensors used were 158 connected to the Arduino, referring to the different scenarios tested: control, low flow rate, and 159 high flow rate.

160

Figure 4. Close up view of the experiment’s test cases.

132 133

135 136

144 145

146 147

148 149

154

𝑉𝑉 = 𝑉𝑉𝑜𝑜𝑜𝑜𝑜𝑜,1 ×₁₀₂₃²⁵ (Equation 1) 155

156 Where is the voltage across the terminals of the sensor measured in volts, and 𝑉𝑉𝑜𝑜𝑜𝑜𝑜𝑜,1is 157 the analog output from the Arduino ranging from 0 to 1023. Each of the three sensors used were 158 connected to the Arduino, referring to the different scenarios tested: control, low flow rate, and 159 high flow rate.

160

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Unlike the voltage and current sensors, no additional computation was required to retrieve the value of the temperature reading from the sensor, however, additional libraries needed to be enabled in the Arduino IDE, namely: the Dallas Temperature library and the One Wire library.

Once these were installed, the data received by the computer served as the recorded temperature in Celsius.

Flow Rate. The volumetric flow rate of water used in the experiment was measured by collecting the amount of water released by each of the cooling systems over a span of one minute as shown in the following equation:

165

𝐼𝐼 = 𝑉𝑉_{𝑜𝑜𝑜𝑜𝑜𝑜,2} ×₁₀₂₃²⁵ (Equation 2) 166

168 𝑉𝑉𝑜𝑜𝑜𝑜𝑜𝑜,2 is the analog output from the Arduino ranging from 0 to 1023. The outputs from these

170

183

187 188

Power Generated

193 194

196 197

Energy Generated

201 202

204 205

(Equation 3)

Where V is the volumetric flow rate in L/min, dv is the change in volume in liters, and dt is the change in time in minutes.

Power Generated

To ensure accuracy of the data, power is computed per data point, which is collected every 1.5 seconds, and not from the averaged-out values of voltage and current, this parameter was computed by utilizing the following equation:

165

𝐼𝐼 = 𝑉𝑉𝑜𝑜𝑜𝑜𝑜𝑜,2 ×₁₀₂₃²⁵ (Equation 2) 166

168 𝑉𝑉𝑜𝑜𝑜𝑜𝑜𝑜,2 is the analog output from the Arduino ranging from 0 to 1023. The outputs from these

170

183

187 188

Power Generated

193 194

196 197

Energy Generated

201 202

204 205

(Equation 4)

Where P is the computed power in watts, V is the measured voltage in volts, and I is the measured current in amperes.

Energy Generated

The total energy generated is taken by getting the power generated and multiplying this to the time this is supplied, as shown in the following equation:

165

𝐼𝐼 = 𝑉𝑉_{𝑜𝑜𝑜𝑜𝑜𝑜,2} ×₁₀₂₃²⁵ (Equation 2) 166

168 𝑉𝑉_{𝑜𝑜𝑜𝑜𝑜𝑜,2} is the analog output from the Arduino ranging from 0 to 1023. The outputs from these

170

183

187 188

Power Generated

193 194

196 197

Energy Generated

201 202

204 205

(Equation 5)

Where E is the total energy generated per given time in watt-hours, P is the computed power in watts, and t is the time that power is supplied.

Comparison and Correlation of Variables Measured

To observe the effect of temperature on power, the correlation between the two variables was determined with the use of Pearson’s correlation coefficient, shown in the following equation:

Comparison and Correlation of Variables Measured

206 To observe the effect of temperature on power, the correlation between the two variables 207 was determined with the use of Pearson’s correlation coefficient, shown in the following equation:

208

𝑟𝑟 = _{√∑(𝑥𝑥}^∑(𝑥𝑥^𝑖𝑖^{−𝑥𝑥̅)(𝑦𝑦}^𝑖𝑖^{−𝑦𝑦̅)}

𝑖𝑖−𝑥𝑥̅)²∑(𝑦𝑦_𝑖𝑖−𝑦𝑦̅)² (Equation 6) 209

210 Where is the Pearson correlation coefficient, 𝑥𝑥_𝑖𝑖 is the value of the x-variable in a sample, 211 𝑥𝑥̅ is the mean of the values of the x-variable, 𝑦𝑦_𝑖𝑖 is the value of the y-variable in a sample, and 𝑦𝑦̅is 212 the mean of the values of the y-variable.

213 The computed Pearson correlation coefficient was evaluated using the guide provided by 214 Mindrilla and Balentyne (2013), presented in Table 2.

215 216

Table 2. Interpretation of Pearson Correlation Coefficient 217 Absolute Value of r Strength of Relationship

Less than 0.3 None or very weak Between 0.3 and 0.5 Weak

Between 0.5 and 0.7 Moderate Greater than 0.7 Strong 218 Results

219 220

The effect of active water cooling on the power output efficiency of a 5-Watt solar PV 221 panel was done by comparing the temperature and electrical output parameters of two active water- 222 cooled cases to an uncooled test case.

223 224

Measured Variables

225 The average values of the measured parameters, namely: temperature, voltage, and power, 226 throughout the 21-day testing period is presented in Table 3. Corresponding line graphs presenting 227 the daily averages of temperature and power output over the testing period are then presented in 228 Figures 6 and 7, respectively.

229

230 Figure 6. Measured average daily temperatures.

231

(Equation 6) Where r is the Pearson correlation coefficient, xi is the value of the x-variable in a sample, x ̅ is the mean of the values of the x-variable, yi is the value of the y-variable in a sample, and y is the ̅ mean of the values of the y-variable.

The computed Pearson correlation coefficient was evaluated using the guide provided by Mindrilla and Balentyne (2013), presented in Table 2.

Table 2. Interpretation of Pearson Correlation Coefficient

Absolute Value of r Strength of Relationship Less than 0.3 None or very weak Between 0.3 and 0.5 Weak

Between 0.5 and 0.7 Moderate Greater than 0.7 Strong

Results

The effect of active water cooling on the power output efficiency of a 5-Watt solar PV panel was done by comparing the temperature and electrical output parameters of two active water-cooled cases to an uncooled test case.

The average values of the measured parameters, namely: temperature, voltage, and power, throughout the 21-day testing period is presented in Table 3. Corresponding line graphs presenting the daily averages of temperature and power output over the testing period are then presented in Figures 6 and 7, respectively.

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Results from the experiment showed that on average, the use of active water cooling can lower rear panel temperatures by almost 8ºC, closely matching the measured average ambient temperature of 31.83ºC. Despite the high flow rate case having doubled that of the low flow rate case, only a marginal difference in temperature was recorded between them, averaging at 0.23ºC.

This similarity becomes more apparent when

observing the trend of both cooled panels in Figure 7, with the lines of each case overlapping across the graph.

Table 3 shows that an increase in flow rate appears to also results in greater values of voltage and power. This leading to the high flow rate case producing the greatest amount of the two parameters followed closely by the low flow rate case, then succeeded by the control

Comparison and Correlation of Variables Measured

206 To observe the effect of temperature on power, the correlation between the two variables 207 was determined with the use of Pearson’s correlation coefficient, shown in the following equation:

208

𝑟𝑟 = _{√∑(𝑥𝑥}^∑(𝑥𝑥^𝑖𝑖^{−𝑥𝑥̅)(𝑦𝑦}^𝑖𝑖^{−𝑦𝑦̅)}

𝑖𝑖−𝑥𝑥̅)²∑(𝑦𝑦_𝑖𝑖−𝑦𝑦̅)² (Equation 6) 209

210 Where is the Pearson correlation coefficient, 𝑥𝑥_𝑖𝑖 is the value of the x-variable in a sample, 211 𝑥𝑥̅ is the mean of the values of the x-variable, 𝑦𝑦_𝑖𝑖 is the value of the y-variable in a sample, and 𝑦𝑦̅is 212 the mean of the values of the y-variable.

213 The computed Pearson correlation coefficient was evaluated using the guide provided by 214 Mindrilla and Balentyne (2013), presented in Table 2.

215 216

Table 2. Interpretation of Pearson Correlation Coefficient 217 Absolute Value of r Strength of Relationship

Less than 0.3 None or very weak Between 0.3 and 0.5 Weak

Between 0.5 and 0.7 Moderate Greater than 0.7 Strong 218 Results

219 220

The effect of active water cooling on the power output efficiency of a 5-Watt solar PV 221 panel was done by comparing the temperature and electrical output parameters of two active water- 222 cooled cases to an uncooled test case.

223 224

225 The average values of the measured parameters, namely: temperature, voltage, and power, 226 throughout the 21-day testing period is presented in Table 3. Corresponding line graphs presenting 227 the daily averages of temperature and power output over the testing period are then presented in 228 Figures 6 and 7, respectively.

229

232 Figure 7. Measured average daily power output per test case.

233 234

Results from the experiment showed that on average, the use of active water cooling can 235 lower rear panel temperatures by almost 8ºC, closely matching the measured average ambient 236 temperature of 31.83ºC. The ambient temperature is also higher than the high flow rate panel 237 temperature compared to the low flow rate as shown on Figure 6. Despite the high flow rate case 238 having doubled that of the low flow rate case, only a marginal difference in temperature was 239 recorded between them, averaging at 0.23ºC. This similarity becomes more apparent when 240 observing the trend of both cooled panels in Figure 7, with the lines of each case overlapping 241 across the graph.

242 243

Table 3. Average values measured for each case throughout the testing period.

244 Case Temperature (ºC) Voltage (V) Current (A) Power (W)

Ambient 31.83

Control 38.77 4.9702 0.2316 1.2675

Low Flow Rate 31.25 5.2968 0.2311 1.3906

High Flow Rate 31.02 5.4928 0.2406 1.4169

245 Table 3 shows that an increase in flow rate appears to also results in greater values of 246 voltage and power. This leading to the high flow rate case producing the greatest amount of the 247 two parameters followed closely by the low flow rate case, then succeeded by the control or 248 uncooled case. On the other hand, current remained almost constant in all three setups. Looking at 249 the daily performance of the panels, Figure 7 also shows the increase in power output from the 250 uncooled panel compared to the panels with the cooling system.

251 Looking at the total energy generated throughout the testing period on Table 4, it was found 252 that the high flow rate case was able to output 238.0420 Wh. The low flow rate on the other hand 253 was able to deliver 233.6211 Wh.

254 255

Table 4. Total energy generated by each case throughout the testing period.

256 Parameter Control Low Flow Rate High Flow Rate

Energy (Wh) 212.9362 233.6211 238.0420

257 258 259

Figure 7. Measured average daily power output per test case.

Table 3. Average values measured for each case throughout the testing period.

Case Temperature (ºC) Voltage (V) Current (A) Power (W)

Ambient 31.83

Control 38.77 4.9702 0.2316 1.2675

Low Flow Rate 31.25 5.2968 0.2311 1.3906

High Flow Rate 31.02 5.4928 0.2406 1.4169

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or uncooled case. On the other hand, current remained almost constant in all three setups.

Looking at the daily performance of the panels, Figure 7 also shows the increase in power output from the uncooled panel compared to the panels with the cooling system.

Looking at the total energy generated throughout the testing period on Table 4, it was found that the high flow rate case was able to output 238.0420 Wh. The low flow rate on the other hand was able to deliver 233.6211 Wh.

Power and Energy of Low and High Flow Rate Cases

From Table 5, it is observed that the the high flow rate case has higher power output and energy yield over the low flow rate case. The difference in actual gains in the power output between the two cases is about 4%, which is significant as the change in effectiveness of a PV module is about 15% (Iqbal et al., 2016).

Results reveal that an increased flow rate for a given PV panel active cooling system results in greater values of power and energy generated.

Correlation of Temperature on Power Generation The resulting Pearson’s correlation coefficient for each day of the testing period is presented below in Table 6.

Table 6. Pearson correlation coefficient between power and temperature.

Day P vs. T (r) Day P vs. T (r)

1 –0.9956 12 –0.9888

2 –0.9684 13 –0.9989

3 –0.9255 14 –0.9276

4 –0.3865 15 –0.9715

5 –0.9796 16 –0.7742

6 –0.9998 17 –0.9994

7 –1.0000 18 –0.9869

8 –0.9813 19 –0.6483

9 –0.9918 20 –0.9991

10 –0.9858 21 –0.9989

11 –0.9969 Average –0.9903

From the values calculated, a strong negative relationship was found to be present between power output and panel temperature. This means that an increase in temperature will lead to a decrease in power generation and energy yield, which was also observed in the results of Tables 3 and 4.

Discussion

The study investigated the effect of an active water cooling system implemented across the top surface of a 5-watt solar PV panel in order to increase its power output. Two different flow rates were tested: a low flow rate of 1.317 L/

min, and a high flow rate of 2.625 L/min. The Table 4. Total energy generated by each case throughout the testing period.

Parameter Control Low Flow Rate High Flow Rate

Energy (Wh) 212.9362 233.6211 238.0420

Table 5. Computed percent changes in average power and total energy for the cooled panels throughout the testing period.

Case Average Percent Change

in Power in Comparison to Control (%)

Percent Change in Total Energy Generated in Comparison to

Control (%)

Low Flow Rate + 8.80 + 9.71

High Flow Rate + 12.22 + 11.79

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53

use of active water cooling was observed to lower the average rear PV panel temperatures by almost 8ºC throughout the testing period of the experiment. Both cooled cases had an average temperature difference of 0.23ºC. Increasing the flow rate also resulted in increased voltage and power values. This finding has also been reported by Luboń et al. (2020) and Gaur and Tiwari (2014) who observed a similarity between the behavior of voltage and power in PV systems. An increase of 8.80% in overall power generation and 9.71%

boost in energy yield was attained by the low flow rate case as compared to the high flow rate case with an increase of 12.22% in power generation and 11.79% in energy yield. This increase in power and energy from a cooling system supports the findings of Lubon et al. (2020) and Dwivedi et al. (2020).

Comparing the relationship between the power output with respect to rear panel temperature, it was found that a strong negative relationship occurs between these two parameters, further implying that higher temperatures result in lower PV panel outputs.

This result was also found to be in line with the PV panel specifications, which state a negative voltage temperature coefficient.

Despite the high flow rate having doubled the amount of the low flow rate, only marginal changes in temperature and gains in performance were attained by the former. This agrees with the results of Santos et al. (2017) and Iqbal et al. (2016). This is also in line with the findings of Saada et al. (2018) that further increases in flow rate used for active water cooling lead to diminishing returns.

As for the parameter that contributes more to power output, results of the study point out to voltage as the contributory factor which affirms the findings of Luboń et al. (2020) and Gaur and Tiwari (2014). Meantime, the value of current remained almost constant for all setups.

The study investigated if continuous cooling would benefit the system throughout its operating period and across different weather conditions. This was done by setting the testing period to a span of 21 consecutive days, operating 8 hours each day. From the data collected, it was observed that the increased voltage output from cooling enhanced the power generation of the PV panels regardless of the weather conditions.

Generally, an increase was observed in the cooled panels in sunny, shady, and rainy days, resulting in an increase of about 12% in power and energy throughout the testing period.

Future studies may want to investigate the effect of irradiance on the power output through the integration of a pyranometer in the setup.

Having data on irradiance would also allow for further insight on how varying light levels affect voltage and current. The gains in power efficiency can also be observed across different levels of irradiance, such as if the increase in yield during low and high levels of irradiance vary or not. Another possibility is to explore on means of optimization with regards to flow rate.

Multiple flow rates could be tested or simulated in order to determine the best balance between energy expended for implementing the water cooling and the energy gained from this, which may allow for the use of active cooling systems in larger scale PV installations.

Conclusion

This study incorporated an active water cooling system in PV panels to lower panel temperature and determine its effect to the power output and energy yield. The results from the testing showed that the cooling system lowered panel temperatures and improved voltage output, while having minimal effect on current. This effect was also observed across varying weather conditions. Having double the flow rate brought only marginal gains to the base flow rate tested. Overall, the implemented active water cooling system is a viable means of increasing the performance of a solar PV system, as lower temperatures result in an increase in power generated, thus enabling greater energy yield.

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