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Maximum Power Point Tracking of Photovoltaic Array on a USV: A Fuzzy Neural-Directed Adaptive Particle Swarm Optimization Approach

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Abstract

Photovoltaic (PV) array equipped on an unmanned surface vehicle (USV) suffers from rapid-changing partial-shading conditions since USV maneuvers frequently alter shadows on deck, thereby facing a challenge in time-varying maximum power point tracking (MPPT). In this paper, a fuzzy neural directed adaptive particle optimization (FN-APSO) solution is innovatively provided to dynamically determine the global maximum power point (GMPP) in a fast-accurate manner. To facilitate the accuracy, an adaptive PSO (APSO) algorithm is created by assigning region-wise update laws which sufficiently avoid unnecessary search behaviors and ensure global convergence, simultaneously. To further enhance the rapidity, using history data, a fuzzy neural network is devised to judge the evolution direction of GMPP, and enables the APSO to incrementally execute, thereby establishing the entire FN-APSO scheme. Simulation results clearly show remarkable MPPT performance in terms of both speed and accuracy under rapid-changing partial-shading conditions.

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Acknowledgements

This work is supported by the Liaoning Revitalization Talents Program (under Grant XLYC1807013), Equipment Pre-Research Fund of Key Laboratory (under Grant 6142215200106).

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Authors and Affiliations

  1. School of Marine Engineering, Dalian Maritime University, Dalian, 116026, People’s Republic of China

    Ning Wang

  2. College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, People’s Republic of China

    Kailin Xu

  3. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300, Nibong Tebal, Pulau Pinang, Malaysia

    Mohd Rizal Arshad

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  1. Ning Wang
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Correspondence to Ning Wang.

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Wang, N., Xu, K. & Arshad, M.R. Maximum Power Point Tracking of Photovoltaic Array on a USV: A Fuzzy Neural-Directed Adaptive Particle Swarm Optimization Approach. Int. J. Fuzzy Syst. 24, 3403–3415 (2022). https://doi.org/10.1007/s40815-022-01335-7

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