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The application of soft computing methods for MPPT of PV system: A technological and status review

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  • Salam, Zainal
  • Ahmed, Jubaer
  • Merugu, Benny S.

Abstract

With the availability of powerful and low cost computing power, maximum power point tracking (MPPT) that utilizes soft computing (SC) techniques are attracting substantial interests from the PV communities. Due to their flexibility and ability to handle non-linear problems, robust SC-based MPPT schemes can be developed. Furthermore, the adaptive in nature SC algorithms is suitable in handling adverse environmental conditions such as partial shading and rapid changes in irradiance. To date, there are several works on MPPT using SC from which we select approximately 45 published works that are directly related to MPPT. However, information on these methods are scattered and there appears to be an absence for a comprehensive review paper on this topic. This work summarizes the current technology and status of SC MPPT as reported in various literature. It also provides an evaluation on the performance of various SC methods based on several criteria, namely PV array dependency, convergence time, ability to handle partial shading conditions, algorithm complexity and hardware/practical implementation. It is envisaged that the information gathered in this paper will be a valuable one-stop source of information for researchers, as well as providing a direction for future research in this area.

Suggested Citation

  • Salam, Zainal & Ahmed, Jubaer & Merugu, Benny S., 2013. "The application of soft computing methods for MPPT of PV system: A technological and status review," Applied Energy, Elsevier, vol. 107(C), pages 135-148.
    • Handle: RePEc:eee:appene:v:107:y:2013:i:c:p:135-148
    DOI: 10.1016/j.apenergy.2013.02.008
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    References listed on IDEAS

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    4. Ishaque, Kashif & Salam, Zainal & Shamsudin, Amir & Amjad, Muhammad, 2012. "A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 99(C), pages 414-422.
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