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Study of the MPP tracking algorithms: Focusing the numerical method techniques

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  • Amir, A.
  • Amir, A.
  • Selvaraj, J.
  • Rahim, N.A.

Abstract

A comparative review between different algorithms for maximum power point (MPP) tracking is presented, particularly focusing Numerical Method (NM) techniques. This paper presents a wide range of efficient NM schemes which have been neglected by most of the MPPT review papers. As, NM techniques are one of the simplest and fastest tracking algorithms. These techniques offer advantages of exact MPP tracking, standalone applications, flexible searching step sizes and no steady state oscillations. In addition, many different MPPT schemes are discussed and compared with the NM techniques. There are many ways of grouping and categorizing the MPPT algorithms for the Photovoltaic (PV) Array. However, evaluation of the NM schemes in comparison with other techniques is provided effectively through analog and digital classification, in terms of implementation and circuitry involved. Therefore, a comparative review majorly focusing on the importance of NM schemes to track the MPP is presented in comparison with other techniques, through analog and digital classification.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:62:y:2016:i:c:p:350-371
    DOI: 10.1016/j.rser.2016.04.039
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    4. Messalti, Sabir & Harrag, Abdelghani & Loukriz, Abdelhamid, 2017. "A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 221-233.
    5. Çelik, Özgür & Teke, Ahmet & Tan, Adnan, 2018. "Overview of micro-inverters as a challenging technology in photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3191-3206.
    6. Amir, Asim & Amir, Aamir & Che, Hang Seng & Elkhateb, Ahmad & Rahim, Nasrudin Abd, 2019. "Comparative analysis of high voltage gain DC-DC converter topologies for photovoltaic systems," Renewable Energy, Elsevier, vol. 136(C), pages 1147-1163.
    7. Julie Viloria-Porto & Carlos Robles-Algarín & Diego Restrepo-Leal, 2018. "A Novel Approach for an MPPT Controller Based on the ADALINE Network Trained with the RTRL Algorithm," Energies, MDPI, vol. 11(12), pages 1-17, December.
    8. Seyedmahmoudian, M. & Horan, B. & Soon, T. Kok & Rahmani, R. & Than Oo, A. Muang & Mekhilef, S. & Stojcevski, A., 2016. "State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 435-455.
    9. Victor Andrean & Pei Cheng Chang & Kuo Lung Lian, 2018. "A Review and New Problems Discovery of Four Simple Decentralized Maximum Power Point Tracking Algorithms—Perturb and Observe, Incremental Conductance, Golden Section Search, and Newton’s Quadratic Int," Energies, MDPI, vol. 11(11), pages 1-25, November.

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