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PV Panel Model Parameter Estimation by Using Neural Network.

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

  • 1Department of Computer Science, Hong Kong Chu Hai College, 80 Castle Peak Road, Castle Peak Bay, Tuen Mun, N.T. Hong Kong, Hong Kong.

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Summary
This summary is machine-generated.

This study introduces a new Neural Network (ANN) algorithm with a Numerical Current Prediction (NCP) layer for estimating photovoltaic (PV) panel model parameters. This method enhances PV panel fault diagnosis and health monitoring.

Keywords:
maximum power pointmodel parameters estimationneural networkphotovoltaic panel

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Area of Science:

  • Renewable Energy Systems
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Photovoltaic (PV) panels are crucial for green energy, necessitating effective performance monitoring and fault diagnosis.
  • Estimating PV panel model parameters is key for assessing panel health and detecting faults.

Purpose of the Study:

  • To propose a novel algorithm for PV panel model parameter estimation using an Artificial Neural Network (ANN) with a Numerical Current Prediction (NCP) layer.
  • To enhance the accuracy of PV panel model parameter estimation for improved fault detection and Maximum Power Point (MPP) tracking.

Main Methods:

  • Observing output voltage and current (VI) signals after load perturbation.
  • Training an ANN to estimate PV panel model parameters.
  • Fine-tuning the ANN with an NCP layer to improve estimation accuracy by approximately 6%.

Main Results:

  • The proposed ANN-NCP system accurately estimates PV panel model parameters from VI signals.
  • The estimated parameters enable effective fault detection and health monitoring.
  • The system aids in tracking operating points for MPP conditions.

Conclusions:

  • The developed ANN-NCP algorithm offers a robust method for PV panel model parameter estimation.
  • This approach significantly contributes to the reliability and efficiency of PV systems through advanced fault diagnosis and performance monitoring.