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A two-steps algorithm improving the P&O steady state MPPT efficiency

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  • Mamarelis, Emilio
  • Petrone, Giovanni
  • Spagnuolo, Giovanni

Abstract

A key point in the control of photovoltaic (PV) systems is the design of the maximum power point tracking (MPPT) algorithm. Although a huge number of approaches has been proposed in literature, the methods based on the perturb and observe (P&O) technique are the most widely employed in commercial products. The reason is in the fact that P&O can be implemented in cheap digital devices by assuring high robustness and a good MPPT efficiency. The low hardware resources required by the P&O algorithm are especially useful in distributed MPPT architectures, where the cost makes the difference. The performances of the P&O algorithm implemented in a digital device are affected by the quantization effect and numerical approximations. In this paper the basic P&O algorithm is suitably improved in order to compensate for these effects. A design recipe for choosing the best values of the P&O parameters is also given. The conclusions of the theoretical analysis are validated through simulations and experiments.

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  • Mamarelis, Emilio & Petrone, Giovanni & Spagnuolo, Giovanni, 2014. "A two-steps algorithm improving the P&O steady state MPPT efficiency," Applied Energy, Elsevier, vol. 113(C), pages 414-421.
    • Handle: RePEc:eee:appene:v:113:y:2014:i:c:p:414-421
    DOI: 10.1016/j.apenergy.2013.07.022
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    3. Yinxiao Zhu & Moon Keun Kim & Huiqing Wen, 2018. "Simulation and Analysis of Perturbation and Observation-Based Self-Adaptable Step Size Maximum Power Point Tracking Strategy with Low Power Loss for Photovoltaics," Energies, MDPI, vol. 12(1), pages 1-20, December.
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    5. Ahmed, Jubaer & Salam, Zainal, 2015. "An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency," Applied Energy, Elsevier, vol. 150(C), pages 97-108.
    6. Noureddine Bouarroudj & Djamel Boukhetala & Vicente Feliu-Batlle & Fares Boudjema & Boualam Benlahbib & Bachir Batoun, 2019. "Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System," Energies, MDPI, vol. 12(14), pages 1-19, July.
    7. Suliang Ma & Mingxuan Chen & Jianwen Wu & Wenlei Huo & Lian Huang, 2016. "Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 9(12), pages 1-24, November.
    8. Amir, A. & Amir, A. & Selvaraj, J. & Rahim, N.A., 2016. "Study of the MPP tracking algorithms: Focusing the numerical method techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 350-371.
    9. Mingxuan Chen & Suliang Ma & Haiyong Wan & Jianwen Wu & Yuan Jiang, 2018. "Distributed Control Strategy for DC Microgrids of Photovoltaic Energy Storage Systems in Off-Grid Operation," Energies, MDPI, vol. 11(10), pages 1-19, October.
    10. Elkin Edilberto Henao-Bravo & Carlos Andrés Ramos-Paja & Andrés Julián Saavedra-Montes & Daniel González-Montoya & Julián Sierra-Pérez, 2020. "Design Method of Dual Active Bridge Converters for Photovoltaic Systems with High Voltage Gain," Energies, MDPI, vol. 13(7), pages 1-31, April.
    11. Kwan, Trevor Hocksun & Wu, Xiaofeng, 2017. "The Lock-On Mechanism MPPT algorithm as applied to the hybrid photovoltaic cell and thermoelectric generator system," Applied Energy, Elsevier, vol. 204(C), pages 873-886.
    12. Kebir, Anouer & Woodward, Lyne & Akhrif, Ouassima, 2019. "Real-time optimization of renewable energy sources power using neural network-based anticipative extremum-seeking control," Renewable Energy, Elsevier, vol. 134(C), pages 914-926.
    13. Boukenoui, R. & Ghanes, M. & Barbot, J.-P. & Bradai, R. & Mellit, A. & Salhi, H., 2017. "Experimental assessment of Maximum Power Point Tracking methods for photovoltaic systems," Energy, Elsevier, vol. 132(C), pages 324-340.
    14. Bradai, R. & Boukenoui, R. & Kheldoun, A. & Salhi, H. & Ghanes, M. & Barbot, J-P. & Mellit, A., 2017. "Experimental assessment of new fast MPPT algorithm for PV systems under non-uniform irradiance conditions," Applied Energy, Elsevier, vol. 199(C), pages 416-429.
    15. Prasanth Ram, J. & Rajasekar, N., 2017. "A new robust, mutated and fast tracking LPSO method for solar PV maximum power point tracking under partial shaded conditions," Applied Energy, Elsevier, vol. 201(C), pages 45-59.
    16. Li, Qiyu & Zhao, Shengdun & Wang, Mengqi & Zou, Zhongyue & Wang, Bin & Chen, Qixu, 2017. "An improved perturbation and observation maximum power point tracking algorithm based on a PV module four-parameter model for higher efficiency," Applied Energy, Elsevier, vol. 195(C), pages 523-537.

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