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PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network.

Wai-Lun Lo¹, Henry Shu-Hung Chung², Richard Tai-Chiu Hsung¹

¹Department of Computer Science, Hong Kong Chu Hai College, Hong Kong, China.

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|May 25, 2024

Summary

This summary is machine-generated.

This study introduces a novel method for estimating photovoltaic (PV) panel parameters using an artificial neural network (ANN) and particle swarm optimization (PSO). The approach enhances accuracy and convergence speed for PV panel health monitoring and maximum power point tracking.

Keywords:

model parameters estimation neural network particle swarm optimization photovoltaic panel

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Area of Science:

Renewable Energy Technologies
Artificial Intelligence in Engineering
Electrical Engineering

Background:

Photovoltaic (PV) panels are crucial green energy sources, necessitating accurate parameter estimation for performance monitoring and fault diagnosis.
Existing methods for PV panel parameter estimation, while advancing, still present opportunities for improved accuracy and efficiency.

Purpose of the Study:

To develop a novel and more accurate approach for estimating photovoltaic (PV) panel parameters.
To enhance the speed of convergence in PV panel parameter estimation algorithms.
To provide improved data for PV panel health monitoring and maximum power point tracking (MMPT).

Main Methods:

Utilizing output current and voltage dynamic responses to create time-series I-V vectors.
Employing an artificial neural network (ANN)-based PV model parameter range classifier (MPRC) trained on diverse I-V datasets.
Integrating MPRC outputs to initialize a particle swarm optimization (PSO) algorithm for parameter estimation.

Main Results:

The proposed hybrid ANN-PSO method achieved up to 3.5% accuracy in PV panel parameter estimation.
Demonstrated significant improvements in the speed of convergence compared to a standalone PSO approach.
Validated through simulations using both experimental and generated I-V datasets.

Conclusions:

The developed method effectively estimates PV panel parameters with enhanced accuracy and faster convergence.
This approach offers a valuable tool for advanced PV panel health monitoring and optimizing maximum power point tracking.
The integration of ANN for initial parameter range classification significantly boosts the efficiency of the PSO algorithm.