A Modified Maximum Power Point Tracking with Constant Power Generation Using Adaptive Neuro-Fuzzy Inference System Algorithm

Indhana Sudiharto; Eka Prasetyono; Anang Budikarso; Safira Fitria Devi

doi:10.22219/kinetik.v7i3.1452

Issue

Vol. 7, No. 3, August 2022

Issue Published : Aug 31, 2022

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

A Modified Maximum Power Point Tracking with Constant Power Generation Using Adaptive Neuro-Fuzzy Inference System Algorithm

https://doi.org/10.22219/kinetik.v7i3.1452

Indhana Sudiharto

Politeknik Elektronika Negeri Surabaya

Eka Prasetyono

Politeknik Elektronika Negeri Surabaya

Anang Budikarso

Politeknik Elektronika Negeri Surabaya

Safira Fitria Devi

Politeknik Elektronika Negeri Surabaya

Corresponding Author(s) : Indhana Sudiharto

indhana@pens.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 7, No. 3, August 2022
Article Published : Aug 30, 2022

Abstract

Renewable energy is being used to lessen the consumption of fossil fuels. Solar energy is a common source of renewable energy. Solar energy is the most promising source of energy due to its long-term sustainability and availability. The output power of solar panels is strongly influenced by the intensity of sunlight and the temperature of the solar panels. Maximum Power Point Tracking (MPPT) control, which aims to optimize the output power of solar panels, is commonly used to increase the efficiency of solar panels. However, MPPT control often causes overvoltage disturbance in systems directly connected to the load. To limit the output power of solar panels, additional Constant Power Generation (CPG) control is required. In this research, a solar panel system will be created to supply submersible DC pumps without any energy storage devices. DC-DC SEPIC Converter is designed with MPPT control combined with CPG control to limit the output power of the converter using the Adaptive Neuro-Fuzzy Inference System method by 150 watts. When the output power of the solar panel is less than the power limit, then MPPT mode will work. While CPG mode works when the PV output power is greater than the limit power. The results of this research showed that the system can provide optimal power generated by solar panels in MPPT mode by increasing efficiency by up to 33.04% and CPG mode can limit power to 150 Watts to avoid overvoltage disturbance at load.

Keywords

Maximum Power Point Tracking Constant Power Generation Adaptive Neuro-Fuzzy Inference System SEPIC Converter

Sudiharto, I., Prasetyono, E., Budikarso, A., & Fitria Devi, S. (2022). A Modified Maximum Power Point Tracking with Constant Power Generation Using Adaptive Neuro-Fuzzy Inference System Algorithm. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 7(3), 219-230. https://doi.org/10.22219/kinetik.v7i3.1452

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References

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Levon Gevorkov, Anton Rassõlkin, Ants Kallaste, and Toomas Vaimann, Simulink Based Model for Flow Control of a Centrifugal Pumping System. International Workshop on Electric Drives: Optimization in Control of Electric Drives (IWED), 2018. https://doi.org/10.1109/IWED.2018.8321399
A. Swandi, S. Rahmadhanningsih, S. Viridi, and I. M. Sutjahja, “Trial of DC Submersible Pump 12 Volt 50 Watt with Solar Power and Relationship between Water Discharge and Storage Height,” JPSE (Journal of Physical Science and Engineering), vol. 6, no. 2, pp. 61–67, Jul. 2021. https://dx.doi.org/10.17977/um024v6i22021p061
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References

M. Senthil Kumar, P. S. Manoharan, and R. Ramachandran, “Modelling and simulation of ANFIS-based MPPT for PV system with modified SEPIC converter,” 2019. http://dx.doi.org/10.1504/IJBIDM.2017.10007894

U. H. al Rasyid, Politeknik Elektronika Negeri Surabaya, Institute of Electrical and Electronics Engineers. Indonesia Section, and Institute of Electrical and Electronics Engineers, A Modified MPPT Algorithm Using Incremental Conductance for Constant Power Generation of Photovoltaic Systems.

S. Shabaan, M. I. Abu El-Sebah, and P. Bekhit, “Maximum power point tracking for photovoltaic solar pump based on ANFIS tuning system,” Journal of Electrical Systems and Information Technology, vol. 5, no. 1, pp. 11–22, May 2018. https://doi.org/10.1016/j.jesit.2018.02.002

Y. Yang, H. Wang, F. Blaabjerg, and T. Kerekes, “A hybrid power control concept for PV inverters with reduced thermal loading,” IEEE Transactions on Power Electronics, vol. 29, no. 12, pp. 6271–6275, 2014. https://doi.org/10.1109/TPEL.2014.2332754

Reza Iskharisma Yuwanda, Eka Prasetyono, and Rachma Prilian Eviningsih, Constant Power Generation Using Modified MPPT P&O to Overcome Overvoltage on Solar Power Plants. 2020 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2020. https://doi.org/10.1109/ISITIA49792.2020.9163685

F. R. Hasan, E. Prasetyono, and E. Sunarno, “A Modified Maximum Power Point Tracking Algorithm Using Grey Wolf Optimization for Constant Power Generation of Photovoltaic System,” 2021. https://doi.org/10.1109/AIMS52415.2021.9466050

R. I. Navarro, “Study of a neural network-based system for stability augmentation of an airplane Annex 1 Introduction to Neural Networks and Adaptive Neuro-Fuzzy Inference Systems (ANFIS),” 2013.

D. Karaboga and E. Kaya, “Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey,” Artificial Intelligence Review, vol. 52, no. 4. Springer Netherlands, pp. 2263–2293, Dec. 01, 2019. https://doi.org/10.1007/s10462-017-9610-2

Daniel W. Hart, “Power Electronics”.

L. M. Septya, I. Sudiharto, S. N. Dwitya, O. Asrarul Qudsi, and E. Sunarno, “Design and Implementation Soft-switching MPPT SEPIC Converter Using P&O Algorithm,” in E3S Web of Conferences, Jun. 2018, vol. 43. https://doi.org/10.1051/e3sconf/20184301010

Soedibyo, Budi Amri, and Mochamad Ashari, The comparative study of Buck-boost, Cuk, Sepic and Zeta converters for maximum power point tracking photovoltaic using P&O method. Int. Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), 2015. https://doi.org/10.1109/ICITACEE.2015.7437823

G. Sharp and A. Emanuel, “Sepic Converter Design and Operation.”

S. Necaibia, M. S. Kelaiaia, H. Labar, A. Necaibia, and E. D. Castronuovo, “Enhanced auto-scaling incremental conductance MPPT method, implemented on low-cost microcontroller and SEPIC converter,” Solar Energy, vol. 180, pp. 152–168, Mar. 2019. https://doi.org/10.1016/j.solener.2019.01.028

A. Faruq, A. Marto, N. K. Izzaty, A. T. Kuye, S. F. Mohd Hussein, and S. S. Abdullah, “Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 1–10, Feb. 2021. https://doi.org/10.22219/kinetik.v6i1.1156

Anang Tjahjono, Ony Asraul Qudsi, Novie Ayub Windarko, Dimas Okky Anggriawan, Ardyono Priyadi, and Mauridhi Hery Purnomo, Photovoltaic Module and Maximum Power Point Tracking Modelling Using Adaptive Neuro-Fuzzy Inference System. Makassar International Conference on Electrical Engineering and Infonnatics (MICEEI), 2014. https://doi.org/10.1109/MICEEI.2014.7067301

N. I. Mufa’ary, I. Sudiharto, and F. D. Murdianto, “Comparison of FLC and ANFIS Methods to Keep Constant Power Based on Zeta Converter,” INTEK: Jurnal Penelitian, vol. 8, no. 1, p. 21, Jul. 2021. http://dx.doi.org/10.31963/intek.v8i1.2701

D. Mlakić and S. Nikolovski, “Anfis as a Method for Determinating MPPT in the Photovoltaic System Simulated in Matlab/Simulink,” 2016. https://doi.org/10.1109/MIPRO.2016.7522301

N. Walia, H. Singh, and A. Sharma, “ANFIS: Adaptive Neuro-Fuzzy Inference System-A Survey,” 2015. http://dx.doi.org/10.5120/ijca2015905635

Yuan-Ting Chu, Li-Qiang Yuan;, and Hsin-Han Chiang, “ANFIS-based Maximum Power Point Tracking Control of PV Modules with DC-DC Converters,” 2016. https://doi.org/10.1109/ICEMS.2015.7385123

J. K. Shiau, Y. C. Wei, and B. C. Chen, “A study on the fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables,” Algorithms, vol. 8, no. 2, pp. 100–127, 2015. https://doi.org/10.3390/a8020100

V. Govinda Chowdary, V. Udhay Sankar, D. Mathew, C. Hussaian Basha, and C. Rani, “Hybrid Fuzzy Logic-Based MPPT for Wind Energy Conversion System,” in Advances in Intelligent Systems and Computing, 2020, vol. 1057, pp. 951–968. https://doi.org/10.1007/978-981-15-0184-5_81

A. M. Noman, K. E. Addoweesh, and A. I. Alolah, “Simulation and Practical Implementation of ANFIS-Based MPPT Method for PV Applications Using Isolated Ćuk Converter,” International Journal of Photoenergy, vol. 2017, 2017. https://doi.org/10.1155/2017/3106734

A. Sangwongwanich, Y. Yang, and F. Blaabjerg, “High-performance constant power generation in grid-connected PV systems,” IEEE Transactions on Power Electronics, vol. 31, no. 3, pp. 1822–1825, Mar. 2016. https://doi.org/10.1109/TPEL.2015.2465151

M. P. Zala, P. H. Pandya, K. N. Odedra, D. P. Patel, and L. Engineering College, “Active Power Control of PV System in MPPT and CPG Mode,” 2017. https://doi.org/10.29007/3f21

Ariya Sangwongwanich, Yongheng Yang, Frede Blaabjerg, and Huai Wang, Benchmarking of Constant Power Generation Strategies for Single-Phase Grid-Connected Photovoltaic Systems. IEEE, 2016. https://doi.org/10.1109/APEC.2016.7467899

Levon Gevorkov, Anton Rassõlkin, Ants Kallaste, and Toomas Vaimann, Simulink Based Model for Flow Control of a Centrifugal Pumping System. International Workshop on Electric Drives: Optimization in Control of Electric Drives (IWED), 2018. https://doi.org/10.1109/IWED.2018.8321399

A. Swandi, S. Rahmadhanningsih, S. Viridi, and I. M. Sutjahja, “Trial of DC Submersible Pump 12 Volt 50 Watt with Solar Power and Relationship between Water Discharge and Storage Height,” JPSE (Journal of Physical Science and Engineering), vol. 6, no. 2, pp. 61–67, Jul. 2021. https://dx.doi.org/10.17977/um024v6i22021p061

F. Alkarrami, T. Iqbal, K. Pope, and G. Rideout, “Dynamic Modelling of Submersible Pump Based Solar Water-Pumping System with Three-Phase Induction Motor Using MATLAB,” Journal of Power and Energy Engineering, vol. 08, no. 02, pp. 20–64, 2020. https://doi.org/10.4236/jpee.2020.82002

Issue

Vol. 7, No. 3, August 2022

A Modified Maximum Power Point Tracking with Constant Power Generation Using Adaptive Neuro-Fuzzy Inference System Algorithm

Corresponding Author(s) : Indhana Sudiharto

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