Main Article Content
This study discusses the design of the Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems employing a modified incremental conductance (IncCond) algorithm to extract maximum power from a PV module. A PV module, a DC-DC converter, and a resistive load constitute the PV system. In the scientific literature, it is well-documented that typical MPPT algorithms have significant drawbacks, such as fluctuations around the MPP and poor tracking during a sudden change in atmospheric conditions. To solve the deficiencies of conventional methodology, a novel modified IncCond method is proposed in this study. The simulation results demonstrate that the updated IncCond algorithm presented allows for less oscillation around the maximum power point (MPP), a rapid dynamic response, and superior performance.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Dhaouadi, G., Djamel, O., Youcef, S., & Salah, C. Implementation of Incremental Conductance Based MPPT Algorithm for Photovoltaic System. Proceedings - 2019 4th International Conference on Power Electronics and Their Applications, ICPEA 2019, 1(September), 1–5. https://doi.org/10.1109/ICPEA1.2019.8911186.
Boumaaraf, H., Talha, A., & Bouhali, O. A three-phase NPC grid-connected inverter for photovoltaic applications using neural network MPPT. Renewable and Sustainable Energy Reviews, 49, 1171–1179. https://doi.org/10.1016/j.rser.2015.04.066.
Y. Chouayet M. Ouassaid. An Experimental Artificial Neural Network Based MPP Tracking for Solar Photovoltaic Systems. In International Conference Europe Middle East & North Africa Information Systems and Technologies to Support Learning. pages 533542. Springer, 2019.
Bouselham, L., Hajji, M., Hajji, B., & Bouali, H. A New MPPT-based ANN for Photovoltaic System under Partial Shading Conditions. EnergyProcedia, 111(September 2016), 924–933. https://doi.org/10.1016/j.egypro.2017.03.255.
Divyasharon, R., NarmathaBanu, R., & Devaraj, D. Artificial Neural Network based MPPT with CUK Converter Topology for PV Systemsunder Varying Climatic Conditions. IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2019, 1–6. https://doi.org/10.1109/INCOS45849.2019.8951321.
Zandi, Z., & Mazinan, A. H. Maximum power point tracking of the solar power plants in shadow mode through artificial neural network. Complex & Intelligent Systems, 5(3), 315–330. https://doi.org/10.1007/s40747-2019-0096-1.
Ounnas, D., Ramdani, M., Chenikher, S., &Bouktir, T. (2017). An Efficient Maximum Power Point Tracking Controller for Photovoltaic Systems Using Takagi–Sugeno Fuzzy Models. Arabian Journal for Science and Engineering, 42(12), 4971–4982.
Abid, H., Tadeo, F., Toumi, A., &Chaabane, M. (2014). MPPT of a photovoltaic panel based on Takagi-Sugeno and fractional algorithms. International Review of Automatic Control, 7(3), 245–253.
Khabou, H., Souissi, M., &Aitouche, A. (2020). MPPT implementation on boost converter by using T–S fuzzy method. Mathematics and Computers in Simulation, 167, 119–134.
Abid, H., Toumi, A., & Chaabane, M. MPPT Algorithm for Photovoltaic Panel Based on Augmented Takagi-Sugeno Fuzzy Model. ISRN Renewable Energy, 2014, 1–10.
Chung, T. M., Daniyal, H., Sulaiman, M. H., & Bakar, M. S. (2016). Comparative study of p&o and modified incremental conductance algorithm in solar maximum power point tracking. IE, T Conference Publications, 2016(CP688). https://doi.org/10.1049/cp.2016.1300.
Elbaset, A. A., Ali, H., Abd-El Sattar, M., & Khaled, M. (2016). Implementation of a modified perturb and observe maximum power point tracking algorithm for photovoltaic system using an embedded microcontroller. IET Renewable Power Generation, 10(4), 551–560.
Lyden, S., &Haque, M. E. (2015). Maximum Power Point Tracking techniques for photovoltaic systems: A comprehensive review and comparative analysis. Renewable and Sustainable Energy Reviews, 52, 1504–1518.
Ilyas, A., Ayyub, M., Khan, M. R., Husain, M. A., & Jain, A. (2018). Hardware Implementation of Perturb and Observe Maximum Power Point Tracking Algorithm for Solar Photovoltaic System. Transactions on Electrical and Electronic Materials, 19(3), 222–229.