Abstract
The main objective of this work is to enhance the performance of the Photovoltaic water pum** system to cover the water requirement in rural areas. To do so, it is important to make sure that the PV array produces its maximum power at all times, which can be influenced by external condition (mainly the temperature and irradiation). Hence, we are employing the Adaptive Neuro-Fuzzy Inference System based MPPT in two ways. The ANFIS controller is considered more accurate and efficient as it uses an artificial neural network to learn from training data and generate fuzzy rules based on that data. Both approaches of ANFIS are used to control the duty cycle of the SEPIC converter, which connects the PV panel to the DC motor feeding the water pump. The system combining the PV panel, the SEPIC converter, the controller and the DC motor, is designed and simulated under MATLAB/Simulink. The performance of the proposed methods is tested under various meteorological conditions.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig9_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig10_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig11_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig12_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig13_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig14_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig15_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig16_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig17_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig18_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig19_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig20_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig21_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig22_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig23_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig24_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig25_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig26_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.3103%2FS0003701X23601734/MediaObjects/11949_2024_5433_Fig27_HTML.png)
Similar content being viewed by others
REFERENCES
Soomar, A.M., Hakeem, A., Messaoudi, M., Musznicki, P., Iqbal, A., and Czapp, S., Solar photovoltaic energy optimization and challenges, Front. Energy Res., 2022, vol. 10, p. 879985.
Kannan, N. and Vakeesan, D., Solar energy for future world: A review, Renewable Sustainable Energy Rev., 2016, vol. 62, pp. 1092–1105.
Loschi, H.J., Iano, Y., León, J., Moretti, A., Conte, F.D., Braga, H., et al., A review on photovoltaic systems: Mechanisms and methods for irradiation tracking and prediction, Smart Grid Renewable Energy, 2015, vol. 6, no. 7, p. 187.
Bollipo, R.B., Mikkili, S., and Bonthagorla, P.K., Hybrid, optimal, intelligent and classical PV MPPT techniques: A review, CSEE J. Power Energy Syst., 2020, vol. 7, no. 1, pp. 9–33.
Miqoi, S., Ougli, A.E., and Tidhaf, B., Design of an adaptive sliding mode controller for efficiency improvement of the MPPT for PV water pum**, Int. J. Intell. Eng. Inf., 2019, vol. 7, no. 1, pp. 19–36.
Javed, M.R., Waleed, A., Virk, U.S., and Hassan, S.Z., Comparison of the adaptive neural-fuzzy interface system (ANFIS) based solar maximum power point tracking (MPPT) with other solar MPPT methods, in 2020 IEEE 23rd International Multitopic Conference (INM-IC), 2020, pp. 1–5. IEEE.
Khaehintung, N., Sirisuk, P., and Kurutach, W., A novel ANFIS controller for maximum power point tracking in photovoltaic systems, in The Fifth International Conference on Power Electronics and Drive Systems (PEDS), 2003, vol. 2, pp. 833–836.
Koochaksaraei, A.A. and Izadfar, H., High-efficiency MPPT controller using ANFIS reference model for solar systems, in 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019, pp. 770–775.
Revathy, S., Kirubakaran, V., Rajeshwaran, M., Balasundaram, T., Sekar, V., Alghamdi, S., Rajab, B.S., Babalghith, A.O., Anbese, E.M., et al., Design and analysis of ANFIS-based MPPT method for solar photovoltaic applications, Int. J. Photoenergy, 2022, vol. 2022, p. 9625564.
Kiprono, A. and Llario, A.I., Solar Pum** for Water Supply: Harnessing Solar Power in Humanitarian and Development, Rugby, UK: Practical Action Publishing, 2020.
Belmahdi, B., Louzazni, M., Akour, M., Cotfas, D.T., Cotfas, P.A., and El Bouardi, A., Long-term global solar radiation prediction in 25 cities in Morocco using the FFNN-BP method, Front. Energy Res., 2021, vol. 9, p. 733842.
Chilundo, R.J., Mahanjane, U.S., and Neves, D., Design and performance of photovoltaic water pum** systems: Comprehensive review towards a renewable strategy for Mozambique, J. Power Energy Eng., 2018, vol. 6, no. 7, pp. 32–63.
Sharma, R., Sharma, S., and Tiwari, S., Design optimization of solar PV water pum** system, Mater. Today: Proc., 2020, vol. 21, pp. 1673–1679. Kumar, R. and Singh, B., Solar PV array fed water pum** system using SEPIC converter based BLDC motor drive, in 2014 Eighteenth National Power Systems Conference (NPSC), 2014, pp. 1–5. IEEE.
Singh, K., Anand, A., Mishra, A.K., Singh, B., and Sahay, K., SEPIC converter for solar PV array fed battery charging in DC homes, J. Inst. Eng. (India): Ser. B, 2021, vol. 102, pp. 455–463.
Mayssa, F. and Sbita, L., Advanced ANFIS-MPPT control algorithm for sunshine photovoltaic pum** systems, in 2012 First International Conference on Renewable Energies and Vehicular Technology, 2012, pp. 167–172.
Sevinc, A., Model parameters of electric motors for desired operating conditions, Adv. Electr. Comput. Eng., 2019, vol. 19, no. 2.
Javed, M.R., Waleed, A., Virk, U.S., and Hassan, S.Z., Comparison of the adaptive neural-fuzzy interface system (ANFIS) based solar maximum power point tracking (MPPT) with other solar MPPT methods, in 2020 IEEE 23rd International Multitopic Conference (INMIC), 2020, pp. 1–5.
Mahdi, A., Mahamad, A., Saon, S., Tuwoso, T., Elmunsyah, H., and Mudjanarko, S., Maximum power point tracking using perturb and observe, fuzzy logic and ANFIS, SN Appl. Sci., 2020, vol. 2, pp. 1–9.
Revathy, S., Kirubakaran, V., Rajeshwaran, M., Balasundaram, T., Sekar, V., Alghamdi, S., Rajab, B.S., Babalghith, A.O., Anbese, E.M., et al., Design and analysis of ANFIS-based MPPT method for solar photovoltaic applications, Int. J. Photoenergy, 2022, vol. 2022, p. 9625564.
Koochaksaraei, A.A. and Izadfar, H., High-efficiency MPPT controller using ANFIS reference model for solar systems, in 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019, pp. 770–775.
Iqbal, A., Abu-Rub, H., and Ahmed, S.M., Adaptive neuro-fuzzy inference system based maximum power point tracking of a solar PV module, in 2010 IEEE International Energy Conference, 2010, pp. 51–56.
Tarek, B., Said, D., and Benbouzid, M., Maximum power point tracking control for photovoltaic system using adaptive neuro-fuzzy “ANFIS”, in 2013 Eighth International Conference and Exhibition on Ecological Vehicles and Renewable Energies (EVER), 2013, pp. 1–7.
Zaghba, L., Khennane, M., Borni, A., Fezzani, A., Bouchakour, A., Mahammed, I.H., and Oudjana, S.H., An enhancement of grid connected PV system performance based on ANFIS MPPT control and dual axis solar tracking, in 2019 1st International Conference on Sustainable Renewable Energy Systems and Applications (ICSRESA), 2019, pp. 1–6.
Mlakić, D., Majdandžić, L., and Nikolovski, S., ANFIS used as a maximum power point tracking algorithm for a photovoltaic system, Int. J. Electr. Comput. Eng. (IJECE), 2018, vol. 8, no. 2, pp. 867–879.
Reddy, K.J. and Sudhakar, N., ANFIS-MPPT control algorithm for a PEMFC system used in electric vehicle applications, Int. J. Hydrogen Energy, 2019, vol. 44, no. 29, pp. 15355–15369.
Aldair, A.A., Obed, A.A., and Halihal, A.F., Design and implementation of ANFIS reference model controller based MPPT using FPGA for photovoltaic system, Renewable Sustainable Energy Rev., 2018, vol. 82, pp. 2202–2217.
Kanwar, K. and Vajpai, D.J., Performance evaluation of different models of PV panel in MATLAB/Simulink environment, Appl. Sol. Energy, 2022, vol. 58, no. 1, pp. 86–94.
Ba, A., Ehssein, C.O., Mahmoud, M.E.M.O.M., Hamdoun, O., and Elhassen, A., Comparative study of different DC/DC power converter for optimal PV system using MPPT (P&O) method, Appl. Sol. Energy, 2018, vol. 54, pp. 235–245.
Mukti, R.J. and Islam, A., Modeling and performance analysis of PV module with maximum power point tracking in MATLAB/Simulink, Appl. Sol. Energy, 2015, vol. 51, pp. 245–252.
Aldjia, L., Mohamed, K., Djalloul, A., and Chaib, A., Energy management and control of a hybrid water pum** system with storage, Appl. Sol. Energy, 2017, vol. 53, pp. 190–198.
Funding
This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
The authors of this work declare that they have no conflicts of interest.
Additional information
Publisher’s Note.
Allerton Press remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Miqoi, S., Tidhaf, B. & El Ougli, A. Two Different Approaches of Applying ANFIS Based MPPT for a PV Water Pum** System with a SEPIC Converter. Appl. Sol. Energy (2024). https://doi.org/10.3103/S0003701X23601734
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.3103/S0003701X23601734