Control of PV Panel System Temperature Using PID Cuckoo Search
Engineering and Technology Journal,
2022, Volume 40, Issue 1, Pages 249-256
AbstractIn this study, the PV panel behavior as a nonlinear system had been studied well. The main contribution of this work was cooling the PV panel temperature to get the optimal power using a PID-CSA controller which was never employed previously in this application. In the beginning, the system has been modeled using three artificial neural network methods which are NARX, NAR and nonlinear input output based on MSE. Then, the PID controller with the intelligent cuckoo search algorithm technique had been studied to accustom PID controller parameters () based on MSE, ASE and IAE. The results exhibited that the best modeling method was NARX with 0.2255 MSE. On the other hand, all the controlling methods were effective and showed an excellent ability to control the system; however, the best method was based on MSE with an error equal to 2.578.
Cuckoo Search Algorithm Optimization
PV Panel Module
- Produce the optimal power using PID-CSA controller by Cooling PV panel temperature.
- For this nonlinear system, NARX technique is the best modelling method based on MSE.
- The best values of the PID controller parameters are accomplished using MSE technique.
 Č. Andrej and F. Andrej, Photovoltaic Systems, IRENA‐ Istria regional energy agency, Pula, Croatia, Rep. 203, (2012).
 Yakup, M. A. B. H. Mohd and A. Q. Malik, Optimum tilt angle and orientation for solar collector in Brunei Darussalam, Renewable Energy, Elsevier, 24 (2001) 223-234.
 L. Mart´ın, L. F. Zarzalejo, J. Polo, A. Navarro, R.Marchante, and M. Cony, Prediction of global solar irradiance based on time series analysis: application to solar thermal power plants energy production planning, Solar Energy, 84 (2010), 1772–1781.
 H. A. Hussien, A. H. Numan and A. R. Abdulmunem Improving of the photovoltaic/thermal system performance using water cooling technique, Materials Science and Engineering, 78 (2015) 012-020.
 S.A. Kalogirou, Artificial neural networks in renewable energy systems applications: a review. Renew. Sustain. Energy Rev., 5 (2001) 373–401.
 M. A. Hamdan, R. A. Haj Khalil, and E. A. M. Abdelhafez, Comparison of neural network models in the estimation of the performance of solar still under Jordanian climate, Journal of Clean Energy Technologies, 1 (2013) 238-242.
 A. Di Piazza, M. C. Di Piazza and G. Vitale, Solar radiation estimate and forecasting by neural networks-based approach, XIII Spanish-Portuguese Conference on Electrical Engineering, Valencia, July,( 2013).
 L. B. Valerio, C. Giuseppina, and D. F. Mariavittoria. Artificial neural networks to predict the power output of a PV panel, Hindawi Publishing Corporation International Journal of Photo Energy, 2014 (2014) 193083.
 S. m. Amin, H. Hizam, M. A. M. Radzi, M. Z. A. Ab Kadir, and M. Maryam, Modelling and prediction of photovoltaic power output using artificial neural networks, Hindawi Publishing Corporation International Journal of Photo Energy, 2014 (2014) 469701.
 H. Parmar, Artificial neural network based modelling of photovoltaic system, International Journal of Latest Trends in Engineering and Technology, 5 (2015) 50-59.
 Sh. Akancha and Kh. Vandana, Modelling and prediction of 150kw pave array system in northern India using artificial neural network, International Journal of Engineering Science Invention, 5 (2016) 18-25.
 Y. Zhou, J. Nie, N. Han, Ch. Chen and Z. Yuen, Study on PID parameters tuning based on particle swarm optimization, Advanced Materials Research 823 (2013) 432-438.
 B. Sasidhar, T. H. Kumar and P.U. Kumar, A comparative analysis of intelligent techniques to obtain MPPT by met heuristic approach in PV systems, International Journal for Modern Trends in Science and Technology, 2(2016) 16-23
 A. Asmi and M. Shinosh, “Cuckoo search algorithm based maximum power point tracking for solar PV systems,” International Journal of Advances in Electrical Power System and Information Technology, 2 (2016) 20-28.
 Ji-Ying Shi, Fei Xue, Zi-Jian Qin, Wen Zhang, Le-Tao Ling and Ting Yang, Improved Global Maximum Power Point Tracking for Photovoltaic System via Cuckoo Search under Partial Shaded Conditions, Journal of Power Electronics, 16 (2016) 287-296.
 I. M. Mohamed, M. O. Abed el-Raouf, M. A. Al-Ahmar and A. B. Fahd, Maximum power point tracking of PV system based cuckoo search algorithm; review and comparison, Energy Procedia, 162 (2019) 117–126.
 A. Ibrahim, A. Raef and S. Obukhov, Maximum power point tracking of partially shading PV system using cuckoo search algorithm, International Journal of Power Electronics and Drive System, 10 (2019) 1081-1089.
 F. K. Abo-Elyousr, A. M. Abdelshafy and A. Y. Abdelaziz, MPPT-based particle swarm and cuckoo search algorithms for PV systems, Springer; 1st ed. Online,( 2019).
 En. Ch. Chang, High-performance pure sine wave inverter with robust intelligent sliding mode maximum power point tracking for photovoltaic applications, Micro Machines, 2020 (2020) 585-600.
 R. Yiannis and L. Iraklis, Application of NARX neural network for predicting marine engine performance parameters, Ships and Offshore Structures, 14 (2019) 1-10.
 S. Ali and M. Maedeh, Assessing linear and nonlinear models to forecast opec oil prices, Revista QUID Special Issue, No. 1, pp. 13-20, (2017).
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