Imran Javed Khan1, Maham Akhlaq 1,*, Faheem Qasim1
1 Department of Electronics, GC University Lahore
Received: 04-Oct-2022 / Revised and Accepted: 05-Oct-2022 / Published On-Line: 06-October-2022
An effective and sustainable solar energy generating system is needed given the current global energy problem. The need to use solar energy properly is necessary because it is one of the most readily available sources of energy. The conversion of solar energy to electric energy uses silicon solar panels, which have an efficiency of between 15% and 20%. The solar cell efficiency is nevertheless decreased by the dust that wind and storms bring. In this study, a sensor-based system is used to monitor the dust buildup on the solar panel and to remove the dust by washing and moistening the panel’s surface. The output conversion efficiency and sensor data are taken into consideration by a fuzzy logic system, which forecasts when the solar panel will need to be cleaned.
Keywords: Solar Panel, Fuzzy Logic, Sensor, Power Conversion Efficiency
The rise in world energy demands require certain clean and sustainable energy resources. Conventionally used energy resource are widely being used as a mean to generate energy [1, 2]. These resources including energy generated by combustion of fossil fuels present inside the earth crust including coal, natural gas and oil . However, since these fuels takes millions of years and a lot of pressure to be converted to useful fuel these resources are considered depleting based on their current usage. Similarly, burning these fuels to convert them to electrical energy results in generation of green-house gases including carbon dioxide and carbon monoxide [4, 5]. These gases are one of the main contributors in the rise in the global temperature and pollution. To minimize the use of conventional energy resource owning to their large number of drawbacks, new and advance technology including renewable energy technology are considered. Renewable energy generation uses sustainable energy resources including solar energy, wind energy, water and biomass in order to generate energy including fuels and electricity [6-9]
As solar energy is readily available across the global during the day. As per NASA, the daily average of solar irradiation that reaches the earth surface is 5.0 kWh/m2. The solar energy is converted to the electricity using photovoltaic effect. The system that is used in order to generate electricity from solar light is solar panel. Solar panel are basic p-n junction silicon semi-conductor materials, which generate an electron hole pair when a photon of light larger than the band-gap of the silicon atom strikes the surface of the solar panel [11-13]. The output voltage and current depends on the fill factor and power output. The output power conversion efficiency in a solar panel depends on the power input and power output. Power input is the amount of power absorbed by the solar panel and the power output is the voltage and current generated by the solar panel.
There are several factors which are considered as the main contributors to solar cell efficiency. The efficiency depends on the amount of photon of light absorbed and number of electron hole pairs generated as a result of the photon of light [14, 15]. Larger the number of electron hole pair generated, larger will be the voltage and current as well as the power conversion efficiency. However, there are several external factors which significantly contribute in decreasing the power conversion efficiency of the solar panel. These factors includes roof orientation, solar shading, climate (snow or heat), Low and high temperature, poor maintenance and dust [16-20]. According to the US Department of Energy, airborne dust particles can lower efficiency by up to 7%, which equates to $10,000 in lifetime value loss for residential solar systems and is substantially greater for large-scale solar farms. In some regions of the world, air pollutants build up, lowering the energy output of solar panels by more than 25%. Dust can be in the form of particulates of dirt and air molecules. According to chemical study, 92% of dirt is dust. Despite making up a lesser portion of the overall detritus in the solar panels, these dust pollutants can nonetheless cause a significant energy loss. Alghamdi et.al, designed a fuzzy control system which automatically clean the solar panel however, it results in waste of water . The fuzzy system provide a parametric estimation based on real life and literature. It acts like a decision tool which depicts the outcomes of the parameters which depends on the input [22-24]. This work provides a simulated control system which has the ability to predict the required of solar panel cleaning only when the solar panel efficiency decreases to a certain level. This system can help in reducing the water losses during the day while cleaning the solar panel.
MATLAB fuzzy rule-based system is used in order to predict the requirement of dust cleaning. The system predicts the requirement of the DC motor operating cleaning system which depends on the output current, voltage and well as the power conversion efficiency. The power conversion efficiency is categorized as the ratio of energy produced to the total solar energy that is absorbed by the solar panel. The output voltage, current as well as the power conversion efficiency depends on the amount of electricity that the solar panel can generate which highly depends on the amount of solar light which can be absorbed by the system. Dust on the solar panel is expected to reduce the power conversion efficiency. DC motor to sprinkle water and clean the solar panel. Dust sensor is used to monitor the dust accumulated on the panel, voltage sensor to predict the output voltage and current sensor to predict the output current.
The proposed system control system in order to continuously monitor the system and create as well as generate a decision based on the change is output for cleaning the solar panel is shown in figure 1.
Fig. 1: Block diagram of the proposed control system
The dust sensor data, voltage monitoring sensor data and current monitoring sensor data are taken as input and the requirement of action is taken as output which changes based on the input parameters. The input and output for the work are added to the FIS of the fuzzy system in MATLAB using MAMDANI model, which is shown in figure 2.
Fig. 2: FIS of the dust monitoring control system
The ranges and membership functions are then added using the membership function editor. The input membership functions and range for the input are shown in figure 3. The range for dust data from the sensor is taken from 0.1 to 0.5 with membership functions of small, medium and large. The range for voltage is taken from 23-37 V with membership functions of small, medium and large. The range for current is taken from 3 to 9 mA with membership functions of small, medium and large.
Fig. 3: Membership functions for input (a) Dust (b) Voltage (c) Current
The range of output action required is taken from 0-1 with membership function small, medium and large as shown in figure 4.
Fig. 4: Membership functions for output “Action Required”
After finalizing the input and output membership functions, the rules are finalized. The number of rules are defined by the number of membership functions and number of rules. The number of rules are equal to m^n where m is the number of membership functions and n is the number of rules. For this work, the number of rules are 27. The rules are defined based on the real life scenarios and literature. The selected rules are shown in table 1.
Table 1: Selected rules for the control system to dust monitoring
|Rule No.||Dust||Voltage||Current||Action Required|
The MATLAB fuzzy system provide the 2D and 3D graph along with the rule viewer in order to shows the impact of input on output.
Results and Discussion
The 2D graph between input and output are shown in figure 5. The impact of current on action required is shown in figure 5 (a). With low current conditions, action is required in terms of removing dust. Low current conditions also represent other factors effecting the solar cell efficiencies. The impact of voltage on action required is shown in figure 5 (b). With low voltage conditions, action is required in terms of removing dust. Low voltage conditions also represent other factors effecting the solar cell efficiencies. The impact of dust accumulated on the solar panel and action required is shown in figure 5 (c). Low dust conditions required minimal to low action requirement. With increase in dust accumulated on the solar panel measured using dust sensor, the requirement of the action increases.
Fig. 5: 2D graph between output actions required with input (a) Current (b) Voltage (c) Dust
3D graph between input voltage and dust with output action required is shown in figure 6 (a). The graph is in accordance with the 2D graph. The decrease in voltage and increase in dust accumulated on the solar panel require action. 3D graph between input current and dust with output action required is shown in figure 6 (b). The graph clearly shows that when the current is low and dust is high, the action required in maximum and the DC motor must operate in order to clear the solar panel from dust accumulated on it.
Fig. 6: 3D for output action required with input (a) Voltage and dust (b) Current and dust
The simulated values from the rules defined and 2D and 3D model is shown in figure 7. The rule viewer shown in figure 7 shows the crisp value of output for a specific value of input. The input values are then used for the calculation using the MAMDANI model formula. The minimum membership function values are calcuated using the equation as shown below,
Fig. 7: Rule viewer for the control system design
Minimum membership function for input 1 Dust are shown in equation 1 and 2
K1 = 0.5-0.44/0.5 = 0.12 (1)
K2 = 1-K1 = 0.88 (2)
Minimum membership function for input 2 Voltage are shown in equation 3 and 4
K3 = 37-28.7/37 = 0.224 V (3)
K4 = 1-K3 = 0.776 V (4)
Minimum membership function for input 3 current are shown in equation 5 and 6
K5 = 9-6/9 = 0.33 mA (5)
K6 = 1 – K5 = 0.67 mA (6)
The calculation for the analysis of the output crisp value based on the minimum membership function and the singelton value is shown in table 2 using the MAMDANI model formula shown in equation 7.
|P = [Σ (Ri × Si) / ΣRi] (7)|
Ri = values of the selected rules
Si = the singleton value ( 0 to 1)
|Dust||Voltage||Current||Action Required||Minimum Membership function value (Ri)||Singelton Value (Si)||
Ri x Si
MAMDANI Model formula calculated value =(∑ 〖Ri x Si〗)/(∑ Ri) x 100
MAMDANI model Formula calcuated value = (0.007474/1.818)*100
MAMDANI model Formula calcuated value = 0.004111 x 100
MAMDANI model Formula calcuated value = 0.4111
Simulated value = 0.404
Difference = 0.00711
Error = 0.7%
The difference between the simulated and calcuated value is less than 1% which shows the accuracy of the work.
The dust accumulated on the solar panel results in decrease in its output voltage and current as well as efficiency. This required cleaning action for dust removal. The proposed system using dust sensor data, output current and voltage data to predict the cleaning action is reported in this work using MATLAB fuzzy rule based system. Fuzzy rule based system predict that the action is minimum which the voltage and current is high. Similarly, the Fuzzy rule based system predict that the action is maximum which the dust accumulated on the panel is high. The different between the simulated and calculated value is less than 1% which shows the accuracy of the work
Author’s Contribution: M.J.K., Conceived the idea; M.J.K., M.A Designed the simulated work and F.Q., did the acquisition of data; M.J.K., M.A., Executed simulated work, data analysis or analysis and interpretation of data and wrote the basic draft; M.A., F.Q., Did the language and grammatical edits or Critical revision.
Funding: The publication of this article was funded by no one.
Conflicts of Interest: The authors declare no conflict of interest.
Acknowledgement: The authors would like to thank the people of Nano-electronics lab, department of Electronics Government College University Lahore for assistance with the simulation of data.
 M. Benghanem, “Optimization of tilt angle for solar panel: Case study for Madinah, Saudi Arabia,” Applied Energy, vol. 88, no. 4, pp. 1427-1433, 2011.
 M. R. Maghami, H. Hizam, C. Gomes, M. A. Radzi, M. I. Rezadad, and S. Hajighorbani, “Power loss due to soiling on solar panel: A review,” Renewable and Sustainable Energy Reviews, vol. 59, pp. 1307-1316, 2016.
 M. S. Mozumder, A.-H. I. Mourad, H. Pervez, and R. Surkatti, “Recent developments in multifunctional coatings for solar panel applications: A review,” Solar Energy Materials and Solar Cells, vol. 189, pp. 75-102, 2019.
 R. Zakharchenko et al., “Photovoltaic solar panel for a hybrid PV/thermal system,” Solar Energy Materials and Solar Cells, vol. 82, no. 1-2, pp. 253-261, 2004.
 R. Arshad, S. Tariq, M. U. Niaz, and M. Jamil, “Improvement in solar panel efficiency using solar concentration by simple mirrors and by cooling,” in 2014 international conference on robotics and emerging allied technologies in engineering (iCREATE), 2014: IEEE, pp. 292-295.
 M. Rosu-Hamzescu and S. Oprea, “Practical guide to implementing solar panel MPPT algorithms,” Microchip Technology Inc, vol. 58, 2013.
 A. M. Bagher, M. M. A. Vahid, and M. Mohsen, “Types of solar cells and application,” American Journal of optics and Photonics, vol. 3, no. 5, pp. 94-113, 2015.
 M. R. S. Shaikh, “A review paper on electricity generation from solar energy,” 2017.
 Z. Wu, Y. Hu, J. X. Wen, F. Zhou, and X. Ye, “A review for solar panel fire accident prevention in large-scale PV applications,” IEEE Access, vol. 8, pp. 132466-132480, 2020.
 W. Javed, Y. Wubulikasimu, B. Figgis, and B. Guo, “Characterization of dust accumulated on photovoltaic panels in Doha, Qatar,” Solar Energy, vol. 142, pp. 123-135, 2017.
 K. Terashima, H. Sato, and T. Ikaga, “Development of an environmentally friendly PV/T solar panel,” Solar Energy, vol. 199, pp. 510-520, 2020.
 A. Parikh, F. Pathan, B. Rathod, and S. Shah, “Solar panel condition monitoring system based on wireless sensor network,” International Journal of Science, Engineering and Technology Research (IJSETR), vol. 4, no. 12, pp. 4320-4324, 2015.
 S. Tayyaba et al., “Simulation, analysis, and characterization of calcium-doped ZnO nanostructures for dye-sensitized solar cells,” Energies, vol. 13, no. 18, p. 4863, 2020.
 M. K. Hossain et al., “A comparative study on the influence of pure anatase and Degussa-P25 TiO2 nanomaterials on the structural and optical properties of dye sensitized solar cell (DSSC) photoanode,” Optik, vol. 171, pp. 507-516, 2018.
 S. A. Sulaiman, H. H. Hussain, N. Leh, and M. S. Razali, “Effects of dust on the performance of PV panels,” World Academy of Science, Engineering and Technology, vol. 58, no. 2011, pp. 588-593, 2011.
 S. A. Abdulgafar, O. S. Omar, and K. M. Yousif, “Improving the efficiency of polycrystalline solar panel via water immersion method,” International Journal of Innovative Research in Science, Engineering and Technology, vol. 3, no. 1, pp. 8127-8132, 2014.
 S. Mehrotra, P. Rawat, M. Debbarma, and K. Sudhakar, “Performance of a solar panel with water immersion cooling technique,” International Journal of Science, Environment and Technology, vol. 3, no. 3, pp. 1161-1172, 2014.
 S. Nada and D. El-Nagar, “Possibility of using PCMs in temperature control and performance enhancements of free stand and building integrated PV modules,” Renewable energy, vol. 127, pp. 630-641, 2018.
 Q. Hassan, M. Jaszczur, E. Przenzak, and J. Abdulateef, “The PV cell temperature effect on the energy production and module efficiency,” Contemporary Problems of Power Engineering and Environmental Protection, vol. 33, p. 1, 2016.
 P. Dash and N. Gupta, “Effect of temperature on power output from different commercially available photovoltaic modules,” International Journal of Engineering Research and Applications, vol. 5, no. 1, pp. 148-151, 2015.
 A. S. Alghamdi, A. S. Bahaj, L. S. Blunden, and Y. Wu, “Dust Removal from Solar PV Modules by Automated Cleaning Systems,” Energies, vol. 12, no. 15, p. 2923, 2019. [Online]. Available: https://www.mdpi.com/1996-1073/12/15/2923.
 C. Huang et al., “Development of Intelligent Solar Panel Cleaning System with Fuzzy Logic Theorem,” Applied Mechanics and Materials, vol. 479-480, pp. 565-569, 12/01 2013, doi: 10.4028/www.scientific.net/AMM.479-480.565.
 M. F. Wasim, M. W. Ashraf, S. Tayyaba, B. Ali, and N. Afzulpurkar, “Nano Generator Simulation Using Fuzzy Logic,” Journal of Engineering Research and Technology, vol. 2, no. 4, 2016.
 B. Ali, M. W. Ashraf, S. Tayyaba, and M. F. Wasim, “MEMS Based Energy Harvesting Controler Using Fuzzy Logic,” Journal of Engineering Research and Technology, vol. 2, no. 3, 2016.