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A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems

Automatika, 2021-01, Vol.62 (1), p.1-20 [Peer Reviewed Journal]

2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2020 ;2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution – Non-Commercial License http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0005-1144 ;EISSN: 1848-3380 ;DOI: 10.1080/00051144.2020.1834062

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  • Title:
    A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems
  • Author: Premkumar, M. ; Kumar, C. ; Sowmya, R. ; Pradeep, J.
  • Subjects: Algorithms ; hybrid GWO ; hybrid WOA ; Maximum power tracking ; MPP ; Optimization ; P&O ; partial shading condition ; Photovoltaic cells ; Simulation ; SSA
  • Is Part Of: Automatika, 2021-01, Vol.62 (1), p.1-20
  • Description: The photovoltaic (PV) systems must work at the maximum power point (MPP) to derive the highest possible power with the higher performance during a change in operating conditions. The primary objective is to implement a novel hybrid tracking algorithm to extract the maximum output power from the solar PV panel or array under partial shading conditions (PSCs). This hybrid MPP tracking algorithm is based on the salp swarm algorithm (SSA), which finds the initial global peak (GP) operating point and is followed by the perturb and observation (P&O) algorithm in the last stage to realize a faster convergence rate. Thus, the computational burden met by the conventional methods such as standalone P&O, hybrid grey-wolf-optimization (HGWO), and hybrid whale-optimization algorithm (HWOA) algorithm reported in the literature is overcome by the proposed hybrid SSA algorithm called HSSA. The P&O algorithm searches the MPP in the projected search space by the SSA algorithm. The proposed hybrid algorithm is simulated using MATLAB/Simulink simulation tool to validate the effectiveness of tracking the MPP. The hybrid SSA is compared with the standalone P&O, hybrid WOA, and hybrid GWO, and from the simulation results, it is proved that the hybrid tracking algorithm exhibits a high tracking performance.
  • Publisher: Ljubljana: Taylor & Francis
  • Language: English;Croatian
  • Identifier: ISSN: 0005-1144
    EISSN: 1848-3380
    DOI: 10.1080/00051144.2020.1834062
  • Source: Taylor & Francis Open Access
    Alma/SFX Local Collection
    ProQuest Central
    DOAJ Directory of Open Access Journals
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