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A novel intelligent optimization-based maximum power point tracking control of photovoltaic system under partial shading conditions

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Abstract

Due to its abundant natural supply and environmentally friendly features, solar photovoltaic (PV) production based on renewable energy is the ideal substitute for conventional energy sources. The efficiency of solar power generation under partial shading conditions (PSCs) is significantly increased by maximizing power extraction from the PV system. The maximum power point tracking (MPPT) method is to track maximum PowerPoint (MPP). This research proposes a photovoltaic MPPT control in partial shading conditions using Loxo-Canis (LOXOCAN) optimization algorithm. The ultimate goal of the novel method is to track the solar photovoltaic system’s maximum power point under conditions of partial shading using the LOXOCAN algorithm. The proposed LOXOCAN algorithm is a combination of Elephant-herd optimization (EHO) and Coyote Optimization Algorithm (COA). The \(K_{p} ,K_{i} ,\) and \(K_{d}\) parameters of the PID controller of the MPPT controller will be tuned to their optimum values using the proposed optimization strategy. Higher MPPT performance and a quick convergence at the global maxima are shown in the proposed Loxo-Canis approach. Also, the recommended hybrid Loxo-Canis MPPT approach offers faster MPPT, less computational work, and higher efficiency.

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Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Abbreviations

ChOA:

Chimp optimization algorithm

COA:

Coyote optimization

CS:

Cuckoo search

CSO:

Cat swarm optimization

EGWO:

Enhanced grey wolf optimization

EHO:

Elephant-herd optimization

GMPP:

Global maximum power point

GMPPT:

Global power point tracking

GOA:

Grasshopper optimization algorithm

GWO:

Grey wolf optimization

HC:

Hill climbing

ICSA:

Improved cuckoo search algorithm

INC:

Incremental conductance

LOXOCAN:

Loxo-Canis

MGWO:

Modified grey wolf optimization

MHA:

Meta-heuristic algorithms

MLFO:

Modified Levy flight optimization

MPPT:

Maximum power point tracking

OGWO:

Original GWO

P&O:

Perturb and observe method

P&O:

Perturb and observe

PS:

Partial shading

PSC:

Partial shading condition

PSCs:

Partial shadowing conditions

PSO:

Particle swarm optimization

PV:

Photovoltaic

P–V:

Power–voltage

RES:

Renewable energy sources

SPV:

Solar photovoltaic

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli/Autonomous, Tanjore Main Road, NH67, Near BHEL, Tiruchirappalli, Tamil Nadu, 620015, India

    Mary Beula Aron & Josephine Rathinadurai Louis

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  1. Mary Beula Aron
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  2. Josephine Rathinadurai Louis
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Contributions

AMB conceived the presented idea and designed the analysis. Also, he carried out the experiment and wrote the manuscript with support from RLJ. All authors discussed the results and contributed to the final manuscript. All authors read and approved the final manuscript.

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Correspondence to Mary Beula Aron.

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Aron, M.B., Louis, J.R. A novel intelligent optimization-based maximum power point tracking control of photovoltaic system under partial shading conditions. Analog Integr Circ Sig Process 118, 489–503 (2024). https://doi.org/10.1007/s10470-023-02216-1

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