Hybrid-Intelligent P&O-InC Algorithm with Deterministic Adaptive Weighting for Maximum Power Point Tracking under Dynamic Conditions
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Keywords:
photovoltaic, maximum power point tracking, hybrid MPPT algorithm, adaptive feature-based control, energy efficiency
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Abstract
Electricity generation remains dominated by fossil fuel–based sources, underscoring the necessity to optimize the utilization of solar energy through photovoltaic (PV) systems in support of the Sustainable Development Goals (SDGs). Variations in solar irradiance and temperature significantly influence PV performance, necessitating effective Maximum Power Point Tracking (MPPT) methods. This present study proposes HI-POnIC as an adaptive development of conventional MPPT algorithms using a deterministic, feature-based decision mechanism. The method employed dynamic weighting and adaptive step adjustment to modify the control response to changes in PV operating characteristics, without any reliance on learning processes. Performance of the system was evaluated through convergence analysis, energy and power tracking efficiency, and spatial accuracy assessment. The findings from the simulation demonstrated that HI-POnIC achieved faster convergence and enhanced stability around the maximum power point when compared with conventional methods. Its lightweight and easily implementable adaptive structure has rendered HI-POnIC suitable for PV systems operating under dynamically varying environmental conditions.
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Handaya, D., Bayhan, N., Purwanti, B. S. R., & Ridlwan, H. M. (2025). Hybrid-Intelligent P&O-InC Algorithm with Deterministic Adaptive Weighting for Maximum Power Point Tracking under Dynamic Conditions. Communications in Science and Technology, 10(2), 399–410. https://doi.org/10.21924/cst.10.2.2025.1840
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References
1. IEA, Global share of electricity generation, 2019 – Charts – Data & Statistics, IEA. 2020.
2. K SB, M Y, Coal Fired Power Plants: Emission Problems and Controlling Techniques, J. Earth Sci. Clim. Change. 8 (2017) 1–9.
3. Kumar S, Sadeq A, Renewable Energy Systems, 2024.
4. Energy | Department of Economic and Social Affairs, 2025.
5. Kannan N, Vakeesan D, Solar energy for future world: - A review, Renew. Sustain. Energy Rev. 62 (2016) 1092–105.
6. Pusch A, Römer U, Culcer D, Ekins-Daukes NJ, Energy Conversion Efficiency of the Bulk Photovoltaic Effect, PRX Energy. 2 (2023) 013006.
7. Lüer L, Peters IM, Corre VML, Forberich K, Guldi DM, Brabec CJ, Bypassing the Single Junction Limit with Advanced Photovoltaic Architectures, Advanced Materials. 36 (2025).
8. Dada M, Popoola P, Recent advances in solar photovoltaic materials and systems for energy storage applications: a review, Beni-Suef Univ. J. Basic Appl. Sci. 12 (2023) 1–15.
9. Hossain MJ, Sun M, Davis KO, Photon management in silicon photovoltaic cells: A critical review, Sol. Energy Mater. Sol. Cells. 267 (2024) 112715.
10. Muldarisnur M, Fahendri F, Perdana I, Abdullah Z, Yusfi M, Light absorption enhancement in organic solar cell using non-concentric Ag:SiO2 core-shell nanoparticles, Communications in Science and Technology. 8 (2023) 50–6.
11. Tofigh Rihani A, Ghandchi M, Increasing the Efficiency of Photovoltaic Systems by Using Maximum Power Point Tracking (MPPT), J. Artif. Intell. Electr. Eng. 4 (2015) 45.
12. Verma D, Nema S, Shandilya AM, Dash SK, Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems, Renew Sustain Energy Rev. 54 (2016) 1018–34.
13. Haque A, Maximum Power Point Tracking (MPPT) Scheme for Solar Photovoltaic System, Energy Technol. Policy. 1 (2014) 115–22.
14. Samosir AS, Gusmedi H, Purwiyanti S, Komalasari E, Modeling and Simulation of Fuzzy Logic based Maximum Power Point Tracking (MPPT) for PV Application, Int. J. Electr. Comput. Eng. 8 (2018) 1315–23.
15. Ali A, Hasan AN, Marwala T, Perturb and observe based on fuzzy logic controller maximum power point tracking (MPPT), In: 2014 International Conference on Renewable Energy Research and Application (ICRERA). Milwaukee, WI, USA, 2014, pp. 406–11.
16. Ahmed J, Salam Z, A Maximum Power Point Tracking (MPPT) for PVsystem using Cuckoo Search with partial shading capability, Applied Energy. 119 (2014) 118–30.
17. Yüksek G, Mete AN, A hybrid variable step size MPPT method based on P&o; INC methods, In: 2017 10th International Conference on Electrical and Electronics Engineering (ELECO). Bursa, Turkey, IEEE 2017, pp. 949–53.
18. Buch K, Bhatt C, Patel U, Tekwani PN, A Novel Hybrid MPPT Technique for PSC using Weighted Average Approach, In: 2023 IEEE Texas Power and Energy Conference (TPEC). College Station, TX, USA, 2023, pp. 1–6.
19. Priyadarshi N, Bhaskar MS, Padmanaban S, Blaabjerg F, Azam F, New CUK–SEPIC converter based photovoltaic power system with hybrid GSA–PSO algorithm employing MPPT for water pumping applications, IET Power Electronics. 13 (2020) 2824–30.
20. Fahim SR, Hasanien HM, Turky RA, Aleem SHEA, Ćalasan M, A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction, Energies. 15 (2022) 8941.
21. Stanojević M, Gojanović J, Matavulj P, Živanović S, Organic solar cell physics analyzed by Shockley diode equation, Opt Quant Electron. 52 (2020) 345.
22. Katche ML, Makokha AB, Zachary SO, Adaramola MS, A Comprehensive Review of Maximum Power Point Tracking (MPPT) Techniques Used in Solar PV Systems, Energies. 16 (2023) 2206.
23. Safari A, Mekhilef S, Simulation and Hardware Implementation of Incremental Conductance MPPT With Direct Control Method Using Cuk Converter, IEEE Trans. Ind. Electron. 58 (2011) 1154–61.
24. Irijanti E, Modified Adaptive Support Weight for Stereo Matching, Communications in Science and Technology. 2 (2017).
25. Badan Pengkajian dan Penerapan Teknologi, Test Report BPPT SLX-375-72M Monocrystalline, Tangerang Selatan, Indonesia: The Natıonal Research And Innovatıon Agency (BRIN) 2021. Report No.: 012/LAP.JATEK/B2TKE/BPPT/III/2021.
26. Kabalan A, Jiorle C, Abouelnagga A, A Real-Time Monitoring Device for Assessing Photovoltaic Performance in Residential Settings, Electronics. 14 (2025) 3771.
27. Singh YK, Dubey S, Rajput P, Singh KY, Pandey K, Real-time and modelled performance assessment and validation studies of PV modules operating in varied climatic zones, Energy Built. Environ. 6 (2025) 585–95.
28. Razak A, Irwan YM, Leow WZ, Irwanto M, Safwati I, Zhafarina M, Investigation of the Effect Temperature on Photovoltaic (PV) Panel Output Performance, Int. J. Adv. Sci. Eng. Inf. Technol. 6 (2016) 682–8.
29. Han JY, Li SY, Impact of temperature and solar irradiance in shadow covering scenarios via two-way sensitivity analysis for rooftop solar photovoltaics, Energy Sources Part A Recovery Util. Environ. Eff. 46 (2024) 3165–76.
30. Li Z, Yang J, Dezfuli PAN, Study on the Influence of Light Intensity on the Performance of Solar Cell, Int. J. Photoenergy. 2021 (2021) 6648739.
31. Ryu S, Ha NY, Ahn YH, Park JY, Lee S, Light intensity dependence of organic solar cell operation and dominance switching between Shockley–Read–Hall and bimolecular recombination losses, Sci. Rep. 11 (2021) 16781.
32. Humaidi AJ, Hameed AH, Design and Comparative Study of Advanced Adaptive Control Schemes for Position Control of Electronic Throttle Valve, Information. 10 (2019) 65.
33. Cao C, Hovakimyan N, Design and Analysis of a Novel \cal L_1 Adaptive Control Architecture With Guaranteed Transient Performance, IEEE Trans. Autom. Control. 53 (2008) 586–91.
34. Hansen J, Johansen TA, Transient performance, resetting and filtering in nonlinear multiple model adaptive control, In: Proceedings of the 2004 American Control Conference. 2004, pp. 1223–8 vol.2.
35. Gibson TE, Annaswamy AM, Lavretsky E, Adaptive Systems with Closed–loop Reference Models: Composite control and observer feedback, IFAC Proc. Vol. 46 (2013) 440–5.
36. Yang J, Na J, Gao G, Robust model reference adaptive control for transient performance enhancement, Int. J. Robust Nonlinear Control. 30 (2020) 6207–28.
37. Wang QG, Sun J, Zhang JX, Huang J, Yu J, Dong H, Survey of transient performance control, Control Eng. Pract. 138 (2023) 105559.
38. Shame BH, Ubaidillah, Tjahjana DDDP, Arifin Z, Aziz M, Harwijayanti W, et al., The renewable energy research contribution of Tanzania: A review, Communications in Science and Technology. 9 (2024) 65–86.
39. Masoomi M, Panahi M, Moradihamedani A, Energy-saving mechanisms for evaluating efficiency improvement and losses reduction scenarios in the thermal power plants sector of Iran, Energy Sources Part B Econ. Plan. Policy. 18 (2023) 2215260.
40. Bruno JC, Pulido T, Review and Analysis of Energy Losses and Inverter Sizing in Photovoltaic Plants, Rochester, NY: Social Science Research Network 2024.
41. Widjonarko, Bukhori S, Pratama AP, An Improving Efficiency MPPT in PV Systems with a Modified Voltage Regulator, In: 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA). Surabaya, Indonesia, 2021, pp. 179–83.
42. Hamzah M ilham hasby, Aprillia H, Giyantara A, Hybrid ANN-PSO Based MPPT Optimization for Enhanced Solar Panel Efficiency, ELKHA J. Tek. Elektro. 17 (2025) 32–7.
43. Abdulrazzaq AA, Ali AH, Efficiency Performances of Two MPPT Algorithms for PV System With Different Solar Panels Irradiancess, Int. J. Power Electron. Drive Syst. 9 (2018) 1755–64.
44. Priyadarshi N, Padmanaban S, Holm-Nielsen JB, Bhaskar MS, Azam F, Internet of things augmented a novel PSO-employed modified zeta converter-based photovoltaic maximum power tracking system: hardware realisation, IET Power Electron. 13 (2020) 2775–81.
45. Yannick TL, Juste Constant EE, Pierre Boris Gael E, Andre William B, Luc Leroy MN, Arlin Bruno T, et al., Durability, thermo-physical characteristics, and mechanical strength prediction of green Portland cement matrix incorporating recycled soda-lime glass and lead glass, Heliyon. 10 (2024) e26288.
46. Boroh AW, Kouayep Lawou S, Mfenjou ML, Ngounouno I, Comparison of geostatistical and machine learning models for predicting geochemical concentration of iron: case of the Nkout iron deposit (south Cameroon), J. Afr. Earth Sci. 195 (2022) 104662.
47. Hodson TO, Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not, Geosci. Model Dev. 15 (2022) 5481–7.
48. Khoshvaght H, Permala RR, Razmjou A, Khiadani M, A critical review on selecting performance evaluation metrics for supervised machine learning models in wastewater quality prediction, J. Environ. Chem. Eng. 13 (2025) 119675.
2. K SB, M Y, Coal Fired Power Plants: Emission Problems and Controlling Techniques, J. Earth Sci. Clim. Change. 8 (2017) 1–9.
3. Kumar S, Sadeq A, Renewable Energy Systems, 2024.
4. Energy | Department of Economic and Social Affairs, 2025.
5. Kannan N, Vakeesan D, Solar energy for future world: - A review, Renew. Sustain. Energy Rev. 62 (2016) 1092–105.
6. Pusch A, Römer U, Culcer D, Ekins-Daukes NJ, Energy Conversion Efficiency of the Bulk Photovoltaic Effect, PRX Energy. 2 (2023) 013006.
7. Lüer L, Peters IM, Corre VML, Forberich K, Guldi DM, Brabec CJ, Bypassing the Single Junction Limit with Advanced Photovoltaic Architectures, Advanced Materials. 36 (2025).
8. Dada M, Popoola P, Recent advances in solar photovoltaic materials and systems for energy storage applications: a review, Beni-Suef Univ. J. Basic Appl. Sci. 12 (2023) 1–15.
9. Hossain MJ, Sun M, Davis KO, Photon management in silicon photovoltaic cells: A critical review, Sol. Energy Mater. Sol. Cells. 267 (2024) 112715.
10. Muldarisnur M, Fahendri F, Perdana I, Abdullah Z, Yusfi M, Light absorption enhancement in organic solar cell using non-concentric Ag:SiO2 core-shell nanoparticles, Communications in Science and Technology. 8 (2023) 50–6.
11. Tofigh Rihani A, Ghandchi M, Increasing the Efficiency of Photovoltaic Systems by Using Maximum Power Point Tracking (MPPT), J. Artif. Intell. Electr. Eng. 4 (2015) 45.
12. Verma D, Nema S, Shandilya AM, Dash SK, Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems, Renew Sustain Energy Rev. 54 (2016) 1018–34.
13. Haque A, Maximum Power Point Tracking (MPPT) Scheme for Solar Photovoltaic System, Energy Technol. Policy. 1 (2014) 115–22.
14. Samosir AS, Gusmedi H, Purwiyanti S, Komalasari E, Modeling and Simulation of Fuzzy Logic based Maximum Power Point Tracking (MPPT) for PV Application, Int. J. Electr. Comput. Eng. 8 (2018) 1315–23.
15. Ali A, Hasan AN, Marwala T, Perturb and observe based on fuzzy logic controller maximum power point tracking (MPPT), In: 2014 International Conference on Renewable Energy Research and Application (ICRERA). Milwaukee, WI, USA, 2014, pp. 406–11.
16. Ahmed J, Salam Z, A Maximum Power Point Tracking (MPPT) for PVsystem using Cuckoo Search with partial shading capability, Applied Energy. 119 (2014) 118–30.
17. Yüksek G, Mete AN, A hybrid variable step size MPPT method based on P&o; INC methods, In: 2017 10th International Conference on Electrical and Electronics Engineering (ELECO). Bursa, Turkey, IEEE 2017, pp. 949–53.
18. Buch K, Bhatt C, Patel U, Tekwani PN, A Novel Hybrid MPPT Technique for PSC using Weighted Average Approach, In: 2023 IEEE Texas Power and Energy Conference (TPEC). College Station, TX, USA, 2023, pp. 1–6.
19. Priyadarshi N, Bhaskar MS, Padmanaban S, Blaabjerg F, Azam F, New CUK–SEPIC converter based photovoltaic power system with hybrid GSA–PSO algorithm employing MPPT for water pumping applications, IET Power Electronics. 13 (2020) 2824–30.
20. Fahim SR, Hasanien HM, Turky RA, Aleem SHEA, Ćalasan M, A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction, Energies. 15 (2022) 8941.
21. Stanojević M, Gojanović J, Matavulj P, Živanović S, Organic solar cell physics analyzed by Shockley diode equation, Opt Quant Electron. 52 (2020) 345.
22. Katche ML, Makokha AB, Zachary SO, Adaramola MS, A Comprehensive Review of Maximum Power Point Tracking (MPPT) Techniques Used in Solar PV Systems, Energies. 16 (2023) 2206.
23. Safari A, Mekhilef S, Simulation and Hardware Implementation of Incremental Conductance MPPT With Direct Control Method Using Cuk Converter, IEEE Trans. Ind. Electron. 58 (2011) 1154–61.
24. Irijanti E, Modified Adaptive Support Weight for Stereo Matching, Communications in Science and Technology. 2 (2017).
25. Badan Pengkajian dan Penerapan Teknologi, Test Report BPPT SLX-375-72M Monocrystalline, Tangerang Selatan, Indonesia: The Natıonal Research And Innovatıon Agency (BRIN) 2021. Report No.: 012/LAP.JATEK/B2TKE/BPPT/III/2021.
26. Kabalan A, Jiorle C, Abouelnagga A, A Real-Time Monitoring Device for Assessing Photovoltaic Performance in Residential Settings, Electronics. 14 (2025) 3771.
27. Singh YK, Dubey S, Rajput P, Singh KY, Pandey K, Real-time and modelled performance assessment and validation studies of PV modules operating in varied climatic zones, Energy Built. Environ. 6 (2025) 585–95.
28. Razak A, Irwan YM, Leow WZ, Irwanto M, Safwati I, Zhafarina M, Investigation of the Effect Temperature on Photovoltaic (PV) Panel Output Performance, Int. J. Adv. Sci. Eng. Inf. Technol. 6 (2016) 682–8.
29. Han JY, Li SY, Impact of temperature and solar irradiance in shadow covering scenarios via two-way sensitivity analysis for rooftop solar photovoltaics, Energy Sources Part A Recovery Util. Environ. Eff. 46 (2024) 3165–76.
30. Li Z, Yang J, Dezfuli PAN, Study on the Influence of Light Intensity on the Performance of Solar Cell, Int. J. Photoenergy. 2021 (2021) 6648739.
31. Ryu S, Ha NY, Ahn YH, Park JY, Lee S, Light intensity dependence of organic solar cell operation and dominance switching between Shockley–Read–Hall and bimolecular recombination losses, Sci. Rep. 11 (2021) 16781.
32. Humaidi AJ, Hameed AH, Design and Comparative Study of Advanced Adaptive Control Schemes for Position Control of Electronic Throttle Valve, Information. 10 (2019) 65.
33. Cao C, Hovakimyan N, Design and Analysis of a Novel \cal L_1 Adaptive Control Architecture With Guaranteed Transient Performance, IEEE Trans. Autom. Control. 53 (2008) 586–91.
34. Hansen J, Johansen TA, Transient performance, resetting and filtering in nonlinear multiple model adaptive control, In: Proceedings of the 2004 American Control Conference. 2004, pp. 1223–8 vol.2.
35. Gibson TE, Annaswamy AM, Lavretsky E, Adaptive Systems with Closed–loop Reference Models: Composite control and observer feedback, IFAC Proc. Vol. 46 (2013) 440–5.
36. Yang J, Na J, Gao G, Robust model reference adaptive control for transient performance enhancement, Int. J. Robust Nonlinear Control. 30 (2020) 6207–28.
37. Wang QG, Sun J, Zhang JX, Huang J, Yu J, Dong H, Survey of transient performance control, Control Eng. Pract. 138 (2023) 105559.
38. Shame BH, Ubaidillah, Tjahjana DDDP, Arifin Z, Aziz M, Harwijayanti W, et al., The renewable energy research contribution of Tanzania: A review, Communications in Science and Technology. 9 (2024) 65–86.
39. Masoomi M, Panahi M, Moradihamedani A, Energy-saving mechanisms for evaluating efficiency improvement and losses reduction scenarios in the thermal power plants sector of Iran, Energy Sources Part B Econ. Plan. Policy. 18 (2023) 2215260.
40. Bruno JC, Pulido T, Review and Analysis of Energy Losses and Inverter Sizing in Photovoltaic Plants, Rochester, NY: Social Science Research Network 2024.
41. Widjonarko, Bukhori S, Pratama AP, An Improving Efficiency MPPT in PV Systems with a Modified Voltage Regulator, In: 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA). Surabaya, Indonesia, 2021, pp. 179–83.
42. Hamzah M ilham hasby, Aprillia H, Giyantara A, Hybrid ANN-PSO Based MPPT Optimization for Enhanced Solar Panel Efficiency, ELKHA J. Tek. Elektro. 17 (2025) 32–7.
43. Abdulrazzaq AA, Ali AH, Efficiency Performances of Two MPPT Algorithms for PV System With Different Solar Panels Irradiancess, Int. J. Power Electron. Drive Syst. 9 (2018) 1755–64.
44. Priyadarshi N, Padmanaban S, Holm-Nielsen JB, Bhaskar MS, Azam F, Internet of things augmented a novel PSO-employed modified zeta converter-based photovoltaic maximum power tracking system: hardware realisation, IET Power Electron. 13 (2020) 2775–81.
45. Yannick TL, Juste Constant EE, Pierre Boris Gael E, Andre William B, Luc Leroy MN, Arlin Bruno T, et al., Durability, thermo-physical characteristics, and mechanical strength prediction of green Portland cement matrix incorporating recycled soda-lime glass and lead glass, Heliyon. 10 (2024) e26288.
46. Boroh AW, Kouayep Lawou S, Mfenjou ML, Ngounouno I, Comparison of geostatistical and machine learning models for predicting geochemical concentration of iron: case of the Nkout iron deposit (south Cameroon), J. Afr. Earth Sci. 195 (2022) 104662.
47. Hodson TO, Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not, Geosci. Model Dev. 15 (2022) 5481–7.
48. Khoshvaght H, Permala RR, Razmjou A, Khiadani M, A critical review on selecting performance evaluation metrics for supervised machine learning models in wastewater quality prediction, J. Environ. Chem. Eng. 13 (2025) 119675.