Detection and identification of global maximum power point operation in solar PV applications using a hybrid ELPSO-P&O tracking technique

J. Prasanth Ram, Dhanup S. Pillai, N. Rajasekar, Scott M. Strachan

Research output: Contribution to journalArticle

Abstract

Non-homogeneous irradiation conditions due to environmental changes introduce multiple peaks in non-linear PV characteristics. Hence, to operate PV at the global power point, numerous algorithms have been proposed in the literature. However, due to the insufficient exploitation of control variables, all the MPPT methods presented in literature fail to guarantee Global Maximum Power Point (GMPP) operation. In this paper, a new detection technology to identify global MPP zones using hybrid Enhanced Leader Particle Swarm Optimization (ELPSO) assisted by a conventional Perturb and Observe (P&O) algorithm is proposed. With inherent mutations, ELPSO applied to MPPT excels in exploring global regions at initial stages to determine the global best leader; whilst, P&O is reverted back soon after global solution space is detected. The transition from ELPSO to P&O is mathematically verified and allowed only when ELPSO finds the global optimal zone. Adapting this hybrid strategy, the proposed method has produced interesting results under partial shaded conditions. For further validation, the results of the proposed hybrid ELPSO-P&O are compared with conventional ELPSO and the hybrid PSO-P&O methods. Experimental results along with energy evaluations confirmed the superiority of the ELPSOP&O method in obtaining the maximum available power under all shaded conditions.
LanguageEnglish
Pages1-14
Number of pages14
JournalIEEE Journal of Emerging and Selected Topics in Power Electronics
Early online date22 Feb 2019
DOIs
Publication statusE-pub ahead of print - 22 Feb 2019

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Particle swarm optimization (PSO)
Irradiation

Keywords

  • global power
  • enhanced leader particle swarm optimization (ELPSO)
  • perturb and observe (P&O)
  • partial shaded conditions (PSC)

Cite this

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title = "Detection and identification of global maximum power point operation in solar PV applications using a hybrid ELPSO-P&O tracking technique",
abstract = "Non-homogeneous irradiation conditions due to environmental changes introduce multiple peaks in non-linear PV characteristics. Hence, to operate PV at the global power point, numerous algorithms have been proposed in the literature. However, due to the insufficient exploitation of control variables, all the MPPT methods presented in literature fail to guarantee Global Maximum Power Point (GMPP) operation. In this paper, a new detection technology to identify global MPP zones using hybrid Enhanced Leader Particle Swarm Optimization (ELPSO) assisted by a conventional Perturb and Observe (P&O) algorithm is proposed. With inherent mutations, ELPSO applied to MPPT excels in exploring global regions at initial stages to determine the global best leader; whilst, P&O is reverted back soon after global solution space is detected. The transition from ELPSO to P&O is mathematically verified and allowed only when ELPSO finds the global optimal zone. Adapting this hybrid strategy, the proposed method has produced interesting results under partial shaded conditions. For further validation, the results of the proposed hybrid ELPSO-P&O are compared with conventional ELPSO and the hybrid PSO-P&O methods. Experimental results along with energy evaluations confirmed the superiority of the ELPSOP&O method in obtaining the maximum available power under all shaded conditions.",
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author = "Ram, {J. Prasanth} and Pillai, {Dhanup S.} and N. Rajasekar and Strachan, {Scott M.}",
note = "{\circledC} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
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AU - Pillai, Dhanup S.

AU - Rajasekar, N.

AU - Strachan, Scott M.

N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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N2 - Non-homogeneous irradiation conditions due to environmental changes introduce multiple peaks in non-linear PV characteristics. Hence, to operate PV at the global power point, numerous algorithms have been proposed in the literature. However, due to the insufficient exploitation of control variables, all the MPPT methods presented in literature fail to guarantee Global Maximum Power Point (GMPP) operation. In this paper, a new detection technology to identify global MPP zones using hybrid Enhanced Leader Particle Swarm Optimization (ELPSO) assisted by a conventional Perturb and Observe (P&O) algorithm is proposed. With inherent mutations, ELPSO applied to MPPT excels in exploring global regions at initial stages to determine the global best leader; whilst, P&O is reverted back soon after global solution space is detected. The transition from ELPSO to P&O is mathematically verified and allowed only when ELPSO finds the global optimal zone. Adapting this hybrid strategy, the proposed method has produced interesting results under partial shaded conditions. For further validation, the results of the proposed hybrid ELPSO-P&O are compared with conventional ELPSO and the hybrid PSO-P&O methods. Experimental results along with energy evaluations confirmed the superiority of the ELPSOP&O method in obtaining the maximum available power under all shaded conditions.

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