PV panel model parameter estimation by using neural network

Wai Lun LO, Henry Shu Hung CHUNG, Richard Tai Chiu HSUNG, Hong FU, Tak Wai SHEN

Research output: Contribution to journalArticlespeer-review

2 Citations (Scopus)


Photovoltaic (PV) panels have been widely used as one of the solutions for green energy sources. Performance monitoring, fault diagnosis, and Control of Operation at Maximum Power Point (MPP) of PV panels became one of the popular research topics in the past. Model parameters could reflect the health conditions of a PV panel, and model parameter estimation can be applied to PV panel fault diagnosis. In this paper, we will propose a new algorithm for PV panel model parameters estimation by using a Neural Network (ANN) with a Numerical Current Prediction (NCP) layer. Output voltage and current signals (VI) after load perturbation are observed. An ANN is trained to estimate the PV panel model parameters, which is then fined tuned by the NCP to improve the accuracy to about 6%. During the testing stage, VI signals are input into the proposed ANN-NCP system. PV panel model parameters can then be estimated by the proposed algorithms, and the estimated model parameters can be then used for fault detection, health monitoring, and tracking operating points for MPP conditions. Copyright © 2023 by the authors.

Original languageEnglish
Article number3657
Issue number7
Publication statusPublished - Mar 2023


Lo, W. L., Chung, H. S. H., Hsung, R. T. C., Fu, H., & Shen, T. W. (2023). PV panel model parameter estimation by using neural network. Sensors, 23(7). Retrieved from https://doi.org/10.3390/s23073657


  • Model parameters estimation
  • Neural network
  • Photovoltaic panel
  • Maximum power point


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