Design MPPT with Anfis Method on Zeta Converter  with DC Load

Epyk Sunarno; Indhana Sudiharto; Dian Yolanita

doi:10.22219/kinetik.v8i1.1629

Issue

Vol. 8, No. 1, February 2023

Issue Published : Feb 28, 2023

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

Design MPPT with Anfis Method on Zeta Converter with DC Load

https://doi.org/10.22219/kinetik.v8i1.1629

Epyk Sunarno

Politeknik Elektronika Negeri Surabaya

Indhana Sudiharto

Politeknik Elektronika Negeri Surabaya

Dian Yolanita

Politeknik Elektronika Negeri Surabaya

Corresponding Author(s) : Dian Yolanita

dyolanita@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 8, No. 1, February 2023
Article Published : Feb 28, 2023

Abstract

Maximum power point tracking (MPPT) for PV (Photovoltaic) systems is provided in this research using artificial intelligence-based control. The design of MPPT system with Anfis Method on the Zeta Converter with DC Load is used to optimize the work of the Photovoltaic which will be used for DC load sources. The MPPT process consists of four main stages, namely module training data, determining input and output data, determining the number and type of membership functions and ANFIS training data. Zeta converter works like a buck boost, which can increase or decrease the voltage which is an advantage in designing systems with very volatile Photovoltaic sources. Zeta Converter is used to get higher efficiency, smaller input and output current ripple values and smaller core losses in the inductor. To improve the efficiency of system performance, An MPPT algorithm for the adaptive neuro fuzzy inference system (ANFIS) that is programmed into a microcontroller controls the zeta converter. ANFIS control is used because the response is faster and more effective. The combined simulation's findings demonstrate that the ANFIS control was successful, and the system can now produce the best possible power from Photovoltaic ipanelsiiniMPPT mode by boosting efficiency by up to 19.96%.

Keywords

Solar Panel Zeta Converter MPPT ANFIS Control

Sunarno, E., Sudiharto, I., & Yolanita, D. (2023). Design MPPT with Anfis Method on Zeta Converter with DC Load. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 8(1), 473-484. https://doi.org/10.22219/kinetik.v8i1.1629

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References

Harminil and M.lAshari, "Optimalization of ANFIS-PSO Algorithm Based on MPPT Control for PV System Under Rapidly Changing Weather Condition," 2022 IEEEi International Conference in Power Engineering Application (ICPEA), 2022, pp. 1-6, doi: 10.1109/ICPEA53519.2022.9744674.
M. R. Javed, A. Waleed, U. S. Virk and S. Z. ul Hassan, "Comparison of the Adaptive Neural-Fuzzy Interface System (ANFIS) based Solar Maximum Power Point Tracking (MPPT) with other Solar MPPT Methods," 2020 IEEE 23rd International Multitopic Conference (INMIC), 2020, pp. 1-5, doi: 10.1109/INMIC50486.2020.9318178.
K. Amara et al., "Improved Performance of a PV Solar Panel with Adaptive Neuro Fuzzy Inference System ANFIS based MPPT," 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), 2018, pp. 1098-1101, doi: 10.1109/ICRERA.2018.8566818.
N. Priyadarshi, S. Padmanaban, J. B. Holm-Nielsen, F. Blaabjerg and M. S. Bhaskar, "An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration Under Fluctuating Sun Irradiance," in IEEE Systems Journal, vol. 14, no. 1, pp. 1218-1229, March 2020, doi: 10.1109/JSYST.2019.2949083.
M. Pattnaik, M. Badoni and Y. Tatte, "Design and analysis of adaptive neuro-fuzzy inference system based MPPT technology," 2021 IEEE 18th India Council International Conference (INDICON), 2021, pp. 1-5, doi: 10.1109/INDICON52576.2021.9691525.
M. Palanivel, U. Kaithamalai and P. Parthsarathi, "Performance Assessment of IC and ANFIS based MPPT for PV System using Super Lift Boost Converter," 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2020, pp. 6-11, doi: 10.1109/ICECA49313.2020.9297426.
S. Padmanaban, N. Priyadarshi, M. Sagar Bhaskar, J. B. Holm-Nielsen, V. K. Ramachandaramurthy and E. Hossain, "A Hybrid ANFIS-ABC Based MPPT Controller for PV System With Anti-Islanding Grid Protection: Experimental Realization," in IEEE Access, vol. 7, pp. 103377-103389, 2019, doi:10.1109/ACCESS.2019.2931547.
N. Priyadarshi, V. K. Ramachandaramurthy, S. Padmanaban, F. Azam, A. K. Sharma and J. P. Kesari, "An ANFIS Artificial Technique Based Maximum Power Tracker for Standalone Photovoltaic Power Generation," 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2018, pp. 102-107, doi: 10.1109/ICPEICES.2018.8897386.
A. A. Koochaksaraei and H. Izadfar, "High-Efficiency MPPT Controller Using ANFIS-reference Model For Solar Systems," 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019, pp. 770-775, doi: 10.1109/KBEI.2019.8734965.
K. -Y. Chou, Y. -W. Yeh, Y. -T. Chen, Y. -M. Cheng and Y. -P. Chen, "Adaptive Neuro Fuzzy Inference System Based MPPT Algorithm applied to Photovoltaic Systems Under Partial Shading Conditions," 2020 International Automatic Control Conference (CACS), 2020, pp. 1-6, doi: 10.1109/CACS50047.2020.9289733.
D. Reddy and S. Ramasamy, "An Artificial Intelligent MPPT Controller Based Three Level SEPIC Topology for 1.2kW Solar PV System," 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), 2019, pp. 1-6, doi: 10.1109/i-PACT44901.2019.8960135.
N. Uddin and M. S. Islam, "Optimization of PV Energy Generation based on ANFIS," 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET), 2018, pp. 474-479, doi: 10.1109/ICISET.2018.8745662.
I. Kapur, D. Jain, A. Jain and R. Garg, "Adaptive Neuro Fuzzy Inference System for MPPT in Standalone Solar Photovoltaic System," 2020 IEEE 17th India Council International Conference (INDICON), 2020, pp. 1-6, doi: 10.1109/INDICON49873.2020.9342105.
J. Andrew-Cotter, M. Nasir Uddin and I. K. Amin, "Particle Swarm Optimization based Adaptive Neuro-Fuzzy Inference System for MPPT Control of a Three-Phase Grid-Connected Photovoltaic System," 2019 IEEE International Electric Machines & Drives Conference (IEMDC), 2019, pp. 2089-2094, doi: 10.1109/IEMDC.2019.8785403.
S. D. Al-Majidi, M. F. Abbod and H. S. Al-Raweshidy, "Maximum Power Point Tracking Technique based on a Neural-Fuzzy Approach for Stand-alone Photovoltaic System," 2020 55th International Universities Power Engineering Conference (UPEC), 2020, pp. 1-6, doi: 10.1109/UPEC49904.2020.9209758.
R. O. Gratela et al., "Neuro-Fuzzy based MPPT for Solar PV Panel Hybrid Cooling System," 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), 2019, pp. 1-6, doi: 10.1109/HNICEM48295.2019.9073356.
E. H. M. Ndiaye, A. Ndiaye, M. A. Tankari and G. Lefebvre, "Adaptive Neuro-Fuzzy Inference System Application for The Identification of a Photovoltaic System and The Forecasting of Its Maximum Power Point," 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), 2018, pp. 1061-1067, doi: 10.1109/ICRERA.2018.8566776.
H. R. Iskandar, A. Prasetya, Y. B. Zainal, M. R. Hidayat, E. Taryana and G. Megiyanto, "Comparison Model of Buck-boost and Zeta Converter Circuit using MPPT Control Incremental Conductance Algorithm," 2020 International Conference on Sustainable Energy Engineering and Application (ICSEEA), 2020, pp. 185-190, doi: 10.1109/ICSEEA50711.2020.9306121.
F. Fitriyah, M. Z. Efendi and F. Dwi Murdianto, "Modeling and Simulation of MPPT ZETA Converter Using Human Psychology Optimization Algorithm Under Partial Shading Condition," 2020 International Electronics Symposium (IES), 2020, pp. 14-20, doi: 10.1109/IES50839.2020.9231890.
M. Z. Abdullah, I. Sudiharto and R. P. Eviningsih, "Photovoltaic System MPPT using Fuzzy Logic Controller," 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), 2020, pp. 378-383, doi: 10.1109/iSemantic50169.2020.9234200.
D. R. Yunitasari, E. Sunarno, I. Ferdiansyah, P. A. M. Putra and L. P. S. Raharja, "Implementation of ANN for Optimization MPPT Using Zeta Converter," 2020 3rd International Conference on Information and Communications Technology (ICOIACT), 2020, pp. 153-158, doi: 10.1109/ICOIACT50329.2020.9331990.
Meghna and Y. K. Chauhan, "PV Water Pumping Using Integrated Quadratic Boost Zeta Converter," 2018 International Conference on Power Energy, Environment and Intelligent Control (PEEIC), 2018, pp. 120-125, doi: 10.1109/PEEIC.2018.8665640.
S. iShabaan, 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 iInformation Technology, vol. 5, no. 1, pp. 11–22, May 2018. https://doi.org/10.1016/j.jesit.2018.02.002
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
L. M. Septya, I. Sudiharto, S. N. Dwitya, O. Asrarul iQudsi, and E. Sunarno, “Design and Implementation Soft-switching MPPT SEPIC Converter iUsing P&O Algorithm,” in E3S iWeb of Conferences, Jun. 2018, vol. 43. https://doi.org/10.1051/e3sconf/20184301010
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:iJurnaliPenelitian,ivol.i8, no. 1, p. 21, Jul. 2021. http://dx.doi.org/10.31963/intek.v8i1.2701
I. Sudiharto, E. Prasetyono, A. Budikarso, and S. Fitria Devi, “A Modified Maximum Power Point Tracking with Constant Power Generation Using Adaptive Neuro-FuzzyInference System Algorithm”, KINETIK, vol.7, no. 3, Aug. 2022. https://doi.org/10.22219/kinetik.v7i3.1452
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
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
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
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

References

Harminil and M.lAshari, "Optimalization of ANFIS-PSO Algorithm Based on MPPT Control for PV System Under Rapidly Changing Weather Condition," 2022 IEEEi International Conference in Power Engineering Application (ICPEA), 2022, pp. 1-6, doi: 10.1109/ICPEA53519.2022.9744674.

M. R. Javed, A. Waleed, U. S. Virk and S. Z. ul Hassan, "Comparison of the Adaptive Neural-Fuzzy Interface System (ANFIS) based Solar Maximum Power Point Tracking (MPPT) with other Solar MPPT Methods," 2020 IEEE 23rd International Multitopic Conference (INMIC), 2020, pp. 1-5, doi: 10.1109/INMIC50486.2020.9318178.

K. Amara et al., "Improved Performance of a PV Solar Panel with Adaptive Neuro Fuzzy Inference System ANFIS based MPPT," 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), 2018, pp. 1098-1101, doi: 10.1109/ICRERA.2018.8566818.

N. Priyadarshi, S. Padmanaban, J. B. Holm-Nielsen, F. Blaabjerg and M. S. Bhaskar, "An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration Under Fluctuating Sun Irradiance," in IEEE Systems Journal, vol. 14, no. 1, pp. 1218-1229, March 2020, doi: 10.1109/JSYST.2019.2949083.

M. Pattnaik, M. Badoni and Y. Tatte, "Design and analysis of adaptive neuro-fuzzy inference system based MPPT technology," 2021 IEEE 18th India Council International Conference (INDICON), 2021, pp. 1-5, doi: 10.1109/INDICON52576.2021.9691525.

M. Palanivel, U. Kaithamalai and P. Parthsarathi, "Performance Assessment of IC and ANFIS based MPPT for PV System using Super Lift Boost Converter," 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2020, pp. 6-11, doi: 10.1109/ICECA49313.2020.9297426.

S. Padmanaban, N. Priyadarshi, M. Sagar Bhaskar, J. B. Holm-Nielsen, V. K. Ramachandaramurthy and E. Hossain, "A Hybrid ANFIS-ABC Based MPPT Controller for PV System With Anti-Islanding Grid Protection: Experimental Realization," in IEEE Access, vol. 7, pp. 103377-103389, 2019, doi:10.1109/ACCESS.2019.2931547.

N. Priyadarshi, V. K. Ramachandaramurthy, S. Padmanaban, F. Azam, A. K. Sharma and J. P. Kesari, "An ANFIS Artificial Technique Based Maximum Power Tracker for Standalone Photovoltaic Power Generation," 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2018, pp. 102-107, doi: 10.1109/ICPEICES.2018.8897386.

A. A. Koochaksaraei and H. Izadfar, "High-Efficiency MPPT Controller Using ANFIS-reference Model For Solar Systems," 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019, pp. 770-775, doi: 10.1109/KBEI.2019.8734965.

K. -Y. Chou, Y. -W. Yeh, Y. -T. Chen, Y. -M. Cheng and Y. -P. Chen, "Adaptive Neuro Fuzzy Inference System Based MPPT Algorithm applied to Photovoltaic Systems Under Partial Shading Conditions," 2020 International Automatic Control Conference (CACS), 2020, pp. 1-6, doi: 10.1109/CACS50047.2020.9289733.

D. Reddy and S. Ramasamy, "An Artificial Intelligent MPPT Controller Based Three Level SEPIC Topology for 1.2kW Solar PV System," 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), 2019, pp. 1-6, doi: 10.1109/i-PACT44901.2019.8960135.

N. Uddin and M. S. Islam, "Optimization of PV Energy Generation based on ANFIS," 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET), 2018, pp. 474-479, doi: 10.1109/ICISET.2018.8745662.

I. Kapur, D. Jain, A. Jain and R. Garg, "Adaptive Neuro Fuzzy Inference System for MPPT in Standalone Solar Photovoltaic System," 2020 IEEE 17th India Council International Conference (INDICON), 2020, pp. 1-6, doi: 10.1109/INDICON49873.2020.9342105.

J. Andrew-Cotter, M. Nasir Uddin and I. K. Amin, "Particle Swarm Optimization based Adaptive Neuro-Fuzzy Inference System for MPPT Control of a Three-Phase Grid-Connected Photovoltaic System," 2019 IEEE International Electric Machines & Drives Conference (IEMDC), 2019, pp. 2089-2094, doi: 10.1109/IEMDC.2019.8785403.

S. D. Al-Majidi, M. F. Abbod and H. S. Al-Raweshidy, "Maximum Power Point Tracking Technique based on a Neural-Fuzzy Approach for Stand-alone Photovoltaic System," 2020 55th International Universities Power Engineering Conference (UPEC), 2020, pp. 1-6, doi: 10.1109/UPEC49904.2020.9209758.

R. O. Gratela et al., "Neuro-Fuzzy based MPPT for Solar PV Panel Hybrid Cooling System," 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), 2019, pp. 1-6, doi: 10.1109/HNICEM48295.2019.9073356.

E. H. M. Ndiaye, A. Ndiaye, M. A. Tankari and G. Lefebvre, "Adaptive Neuro-Fuzzy Inference System Application for The Identification of a Photovoltaic System and The Forecasting of Its Maximum Power Point," 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), 2018, pp. 1061-1067, doi: 10.1109/ICRERA.2018.8566776.

H. R. Iskandar, A. Prasetya, Y. B. Zainal, M. R. Hidayat, E. Taryana and G. Megiyanto, "Comparison Model of Buck-boost and Zeta Converter Circuit using MPPT Control Incremental Conductance Algorithm," 2020 International Conference on Sustainable Energy Engineering and Application (ICSEEA), 2020, pp. 185-190, doi: 10.1109/ICSEEA50711.2020.9306121.

F. Fitriyah, M. Z. Efendi and F. Dwi Murdianto, "Modeling and Simulation of MPPT ZETA Converter Using Human Psychology Optimization Algorithm Under Partial Shading Condition," 2020 International Electronics Symposium (IES), 2020, pp. 14-20, doi: 10.1109/IES50839.2020.9231890.

M. Z. Abdullah, I. Sudiharto and R. P. Eviningsih, "Photovoltaic System MPPT using Fuzzy Logic Controller," 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), 2020, pp. 378-383, doi: 10.1109/iSemantic50169.2020.9234200.

D. R. Yunitasari, E. Sunarno, I. Ferdiansyah, P. A. M. Putra and L. P. S. Raharja, "Implementation of ANN for Optimization MPPT Using Zeta Converter," 2020 3rd International Conference on Information and Communications Technology (ICOIACT), 2020, pp. 153-158, doi: 10.1109/ICOIACT50329.2020.9331990.

Meghna and Y. K. Chauhan, "PV Water Pumping Using Integrated Quadratic Boost Zeta Converter," 2018 International Conference on Power Energy, Environment and Intelligent Control (PEEIC), 2018, pp. 120-125, doi: 10.1109/PEEIC.2018.8665640.

S. iShabaan, 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 iInformation Technology, vol. 5, no. 1, pp. 11–22, May 2018. https://doi.org/10.1016/j.jesit.2018.02.002

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

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

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:iJurnaliPenelitian,ivol.i8, no. 1, p. 21, Jul. 2021. http://dx.doi.org/10.31963/intek.v8i1.2701

I. Sudiharto, E. Prasetyono, A. Budikarso, and S. Fitria Devi, “A Modified Maximum Power Point Tracking with Constant Power Generation Using Adaptive Neuro-FuzzyInference System Algorithm”, KINETIK, vol.7, no. 3, Aug. 2022. https://doi.org/10.22219/kinetik.v7i3.1452

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

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

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

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

Issue

Vol. 8, No. 1, February 2023

Design MPPT with Anfis Method on Zeta Converter with DC Load

Corresponding Author(s) : Dian Yolanita

Abstract

Keywords

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References

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