Tracking the photovoltaic maximum power point using an enhanced whale algorithm and a BP neural network

Xiongfei Lang; Siming Wang

doi:10.1117/12.3032884

5 July 2024 Tracking the photovoltaic maximum power point using an enhanced whale algorithm and a BP neural network

Xiongfei Lang, Siming Wang

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131840H (2024) https://doi.org/10.1117/12.3032884
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

The conventional maximum power tracking (MPPT) approach is inapplicable to the problem of the photovoltaic system's output power curve having numerous peaks and many steps as a result of local shading of the solar array under actual illumination. In order to maximize power point tracking, this research suggests an enhanced whale method for BP neural network optimization. Building upon the foundation of the original whale algorithm, this method introduces adaptive inertia weight strategy and chaotic initialization, hence improving the algorithm's ability to exit the local optimal position. Subsequently, the initial parameters are inserted into the training network to anticipate the maximum power point, and the BP neural network's initial connection weights and biases are optimized using the enhanced whale method. Using the enhanced whale algorithm to optimize the BP neural network to realize photovoltaic MPPT has good tracking speed and accuracy, and the oscillation at the maximum power point is clearly minimized, as demonstrated by MATLAB simulation verification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiongfei Lang and Siming Wang "Tracking the photovoltaic maximum power point using an enhanced whale algorithm and a BP neural network", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131840H (5 July 2024); https://doi.org/10.1117/12.3032884

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KEYWORDS

Photovoltaics

Detection and tracking algorithms

Neural networks

Solar cells

Evolutionary algorithms

Mathematical optimization

Solar energy

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