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  • An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models

    Author(s)
    Abbassi, Rabeh
    Abbassi, Abdelkader
    Heidari, Ali Asghar
    Mirjalili, Seyedali
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2019
    Metadata
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    Abstract
    Solar Photovoltaic systems (SPVSs) are becoming one of the most popular renewable energy technology for generating significant share of electric power. With the consistent growth of SPVSs applications, the challenge of parameters estimation of photovoltaic cells has drawn the attention of researchers and industrialists and gained immense momentum for SPVSs modeling. This paper proposes an efficient approach based on Salp Swarm Algorithm (SSA) for extracting the parameters of the electrical equivalent circuit of PV cell based double-diode model. The experimental and comparative results demonstrate that SSA is highly competitive ...
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    Solar Photovoltaic systems (SPVSs) are becoming one of the most popular renewable energy technology for generating significant share of electric power. With the consistent growth of SPVSs applications, the challenge of parameters estimation of photovoltaic cells has drawn the attention of researchers and industrialists and gained immense momentum for SPVSs modeling. This paper proposes an efficient approach based on Salp Swarm Algorithm (SSA) for extracting the parameters of the electrical equivalent circuit of PV cell based double-diode model. The experimental and comparative results demonstrate that SSA is highly competitive with the results of two algorithms that have never been used before for the PV cell parameter extraction namely Sine Cosine Algorithm (SCA) and Virus Colony Search Algorithm (VCS). SSA is also significantly better than three well-established parameter extraction algorithms namely Ant Lion Optimizer (ALO), Gravitational Search Algorithm (GSA) and Whale Optimization Algorithm (WOA). Several evaluation criteria including Mean Square Error (MSE), Absolute Error (AE) and statistical criterion show that the SSA algorithm provides the highest value of accuracy and has merits in designing SPVSs.
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    Journal Title
    Energy Conversion and Management
    Volume
    179
    DOI
    https://doi.org/10.1016/j.enconman.2018.10.069
    Subject
    Mechanical engineering
    Publication URI
    http://hdl.handle.net/10072/383230
    Collection
    • Journal articles

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