Deep-Learning Algorithmic-Based Improved Maximum Power Point-Tracking Algorithms Using Irradiance Forecast
Abstract
:1. Introduction
2. Solar Power Generation System Modeling and Conventional Control Algorithms
2.1. PV Cell Modeling
2.2. Boost Converter (BC) Modeling
2.3. Conventional MPPT Control Algorithm (P&O Algorithm)
3. Proposed Maximum Power Point Tracking Algorithm
4. Deep Learning Algorithm Model Construction
5. Results and Discussion
6. Conclusions
Funding
Data Availability Statement
Conflicts of Interest
References
- Ali, A.I.; Sayed, M.A.; Mohamed, E.E. Modified efficient perturb and observe maximum power point tracking technique for grid-tied PV system. Int. J. Electr. Power Energy Syst. 2018, 99, 192–202. [Google Scholar] [CrossRef]
- Solangi, K.H.; Islam, M.R.; Saidur, R.; Rahim, N.A.; Fayaz, H. A review on global solar energy policy. Renew. Sustain. Energy Rev. 2011, 15, 2149–2163. [Google Scholar] [CrossRef]
- Sayed, M.A.; Mohamed, E.E.; Ali, A.I. Maximum power point tracking technique for grid tie PV system. In Proceedings of the 7th International Middle East Power Systems Conference (MEPCON’15), Mansoura, Egypt, 15–17 December 2015. [Google Scholar]
- Ali, A.I.M.; Sayed, M.A.; Takeshita, T.; Hassan, A.M.M.; Azmy, A.M. A single-phase modular multilevel inverter based on controlled DC-cells under two SPWM techniques for renewable energy applications. Int. Trans. Electr. Energy Syst. 2020, 31, e12599. [Google Scholar] [CrossRef]
- Hanzaei, S.H.; Gorji, S.A.; Ektesabi, M. A Scheme-Based Review of MPPT Techniques with Respect to Input Variables Including Solar Irradiance and PV Arrays’ Temperature. IEEE Access 2020, 8, 182229–182239. [Google Scholar] [CrossRef]
- Nofuentes, G.; Gueymard, C.; Aguilera, J.; Pérez-Godoy, M.; Charte, F. Is the average photon energy a unique characteristic of the spectral distribution of global irradiance? Sol. Energy 2017, 149, 32–43. [Google Scholar] [CrossRef]
- Gunasekaran, M.; Krishnasamy, V.; Selvam, S.; Almakhles, D.J.; Anglani, N. An Adaptive Resistance Perturbation Based MPPT Algorithm for Photovoltaic Applications. IEEE Access 2020, 8, 196890–196901. [Google Scholar] [CrossRef]
- Hirata, Y.; Aihara, K. Improving time series prediction of solar irradiance after sunrise: Comparison among three methods for time series prediction. Sol. Energy 2017, 149, 294–301. [Google Scholar] [CrossRef]
- Huynh, D.C.; Dunnigan, M.W. Development and Comparison of an Improved Incremental Conductance Algorithm for Tracking the MPP of a Solar PV Panel. IEEE Trans. Sustain. Energy 2016, 7, 1421–1429. [Google Scholar] [CrossRef]
- Ishaque, K.; Salam, Z. A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition. Renew. Sustain. Energy Rev. 2013, 19, 475–488. [Google Scholar] [CrossRef]
- Eltawil, M.A.; Zhao, Z. MPPT techniques for photovoltaic applications. Renew. Sustain. Energy Rev. 2013, 25, 793–813. [Google Scholar] [CrossRef]
- Ali, A.I.; Sayed, M.A.; Mohamed, E.E. Maximum PowerPoint Tracking technique applied on partial shaded grid connected PV system. In Proceedings of the Eighteenth International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, 27–29 December 2016; Volume 2016, pp. 656–663. [Google Scholar]
- Ali, A.I.M.; Sayed, M.A.; Takeshita, T. Isolated single-phase single-stage DC-AC cascaded transformer-based multilevel inverter for stand-alone and grid-tied applications. Int. J. Electr. Power Energy Syst. 2020, 125, 106534. [Google Scholar] [CrossRef]
- Ali, A.I.M.; Sayed, M.A.; Takeshita, T. Analysis and design of high-power single-stage three-phase differential-based flyback inverter for photovoltaic applications. In Proceedings of the 2020 22nd European Conference on Power Electronics and Applications (EPE’20 ECCE Europe), Lyon, France, 7–11 September 2020; Volume 2020, pp. 1–8. [Google Scholar]
- Ahmed, J.; Salam, Z. An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency. Appl. Energy 2015, 150, 97–108. [Google Scholar] [CrossRef]
- Tafticht, T.; Agbossou, K.; Doumbia, M.; Chériti, A. An improved maximum power point tracking method for photovoltaic systems. Renew. Energy 2008, 33, 1508–1516. [Google Scholar] [CrossRef]
- Elgendy, M.A.; Zahawi, B.; Atkinson, D.J. Assessment of the Incremental Conductance Maximum Power Point Tracking Algorithm. IEEE Trans. Sustain. Energy 2012, 4, 108–117. [Google Scholar] [CrossRef]
- Liu, Y.-H.; Huang, S.-C.; Huang, J.-W.; Liang, W.-C. A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions. IEEE Trans. Energy Convers. 2012, 27, 1027–1035. [Google Scholar] [CrossRef]
- Kamran, M.; Mudassar, M.; Fazal, M.R.; Asghar, M.U.; Bilal, M.; Asghar, R. Implementation of improved Perturb & Observe MPPT technique with confined search space for standalone photovoltaic system. J. King Saud Univ. Eng. Sci. 2018, 32, 432–441. [Google Scholar] [CrossRef]
- Ghassami, A.A.; Sadeghzadeh, S.M.; Soleimani, A. A high performance maximum power point tracker for PV systems. Int. J. Electr. Power Energy Syst. 2013, 53, 237–243. [Google Scholar] [CrossRef]
- Belkaid, A.; Colak, I.; Isik, O. Photovoltaic maximum power point tracking under fast varying of solar radiation. Appl. Energy 2016, 179, 523–530. [Google Scholar] [CrossRef]
- Bayod-Rújula, Á.; Cebollero-Abián, J.-A. A novel MPPT method for PV systems with irradiance measurement. Sol. Energy 2014, 109, 95–104. [Google Scholar] [CrossRef]
- Fathabadi, H. Novel fast dynamic MPPT (maximum power point tracking) technique with the capability of very high accurate power tracking. Energy 2016, 94, 466–475. [Google Scholar] [CrossRef]
- Rizzo, S.A.; Scelba, G. ANN based MPPT method for rapidly variable shading conditions. Appl. Energy 2015, 145, 124–132. [Google Scholar] [CrossRef]
- Sutton, R.S.; Barto, A.G. Reinforcement Learning: An Introduction, 2nd ed.; MIT Press: Cambridge, MA, USA, 2018. [Google Scholar]
- Glavic, M. (Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives. Annu. Rev. Control 2019, 48, 22–35. [Google Scholar] [CrossRef] [Green Version]
- Kofinas, P.; Doltsinis, S.; Dounis, A.; Vouros, G. A reinforcement learning approach for MPPT control method of photovoltaic sources. Renew. Energy 2017, 108, 461–473. [Google Scholar] [CrossRef]
- Hsu, R.C.; Liu, C.-T.; Chen, W.-Y.; Hsieh, H.-I.; Wang, H.-L. A Reinforcement Learning-Based Maximum Power Point Tracking Method for Photovoltaic Array. Int. J. Photoenergy 2015, 2015, 1–12. [Google Scholar] [CrossRef]
- Youssef, A.; Telbany, M.E.; Zekry, A. Reinforcement Learning for Online Maximum Power Point Tracking Control. J. Clean Energy Technol. 2015, 4, 245–248. [Google Scholar] [CrossRef] [Green Version]
- Zhang, X.; Li, S.; He, T.; Yang, B.; Yu, T.; Li, H.; Jiang, L.; Sun, L. Memetic reinforcement learning based maximum power point tracking design for PV systems under partial shading condition. Energy 2019, 174, 1079–1090. [Google Scholar] [CrossRef]
- Ding, M.; Lv, D.; Yang, C.; Li, S.; Fang, Q.; Yang, B.; Zhang, X. Global Maximum Power Point Tracking of PV Systems under Partial Shading Condition: A Transfer Reinforcement Learning Approach. Appl. Sci. 2019, 9, 2769. [Google Scholar] [CrossRef] [Green Version]
- Blinov, A.; Korkh, O.; Chub, A.; Vinnikov, D.; Peftitsis, D.; Norrga, S.; Galkin, I. High Gain DC–AC High-Frequency Link Inverter with Improved Quasi-Resonant Modulation. IEEE Trans. Ind. Electron. 2021, 69, 1465–1476. [Google Scholar] [CrossRef]
- Ali, A.I.M.; Mohamed, H.R.A. Improved P&O MPPT algorithm with efficient open-circuit voltage estimation for two-stage grid-integrated PV system under realistic solar radiation. Int. J. Electr. Power Energy Syst. 2022, 137, 107805. [Google Scholar] [CrossRef]
- Tey, K.S.; Mekhilef, S. Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level. Sol. Energy 2014, 101, 333–342. [Google Scholar] [CrossRef]
- Houssamo, I.; Locment, F.; Sechilariu, M. Maximum power tracking for photovoltaic power system: Development and experimental comparison of two algorithms. Renew. Energy 2010, 35, 2381–2387. [Google Scholar] [CrossRef]
- Bründlinger, R.; Henze, N.; Häberlin, H.; Burger, B.; Bergmann, A.; Baumgartner, F. prEN 50530—The new European standard for performance characterisation of PV inverters. In Proceedings of the 24th European Photovoltaic Solar Energy Conference, Hamburg, Germany, 21–25 September 2009; WIP Wirtschaft und Infrastruktur GmbH: Hamburg, Germany, 2009; pp. 3105–3109. [Google Scholar]
Maximum Power, PMPP | 100 (W) |
---|---|
Voltage at MPP, VMPP | 18.00 (V) |
Current at MPP, IMPP | 5.56 (A) |
Open circuit voltage, VOC | 22.50 (V) |
Short circuit current, ISC | 5.81 (A) |
Temperature, ate STC | 25 °C |
Description | DC-DC Boost Converter |
---|---|
Input capacitor (Cin) | 200 μF |
Output capacitor (Cf) | 20 μF |
Output inductor (Lf) | 15 mH |
Switching frequency | 10 kHz |
SS-PO | LS-PO | Proposed | ||
---|---|---|---|---|
PV voltage (V) | Mean | 14.94 | 14.94 | 14.45 |
Standard deviation | 1.53 | 1.53 | 0.03 | |
Efficiency (%) | Mean | 87.37 | 87.14 | 98.38 |
Standard deviation | 9.77 | 10.23 | 0.34 | |
Transient time (s) | - | 0.63 | 0.19 | 0.07 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Roh, C. Deep-Learning Algorithmic-Based Improved Maximum Power Point-Tracking Algorithms Using Irradiance Forecast. Processes 2022, 10, 2201. https://doi.org/10.3390/pr10112201
Roh C. Deep-Learning Algorithmic-Based Improved Maximum Power Point-Tracking Algorithms Using Irradiance Forecast. Processes. 2022; 10(11):2201. https://doi.org/10.3390/pr10112201
Chicago/Turabian StyleRoh, Chan. 2022. "Deep-Learning Algorithmic-Based Improved Maximum Power Point-Tracking Algorithms Using Irradiance Forecast" Processes 10, no. 11: 2201. https://doi.org/10.3390/pr10112201