PV Panel Model Parameter Estimation by Using Neural Network

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.

Automated summary

In this paper, a new algorithm for parameter estimation of photovoltaic (PV) panels is proposed using a neural network with a numerical current prediction layer. The algorithm trains the neural network to estimate the PV panel model parameters based on observed voltage and current signals after load perturbation. The proposed algorithm achieves an accuracy improvement of about 6% by fine-tuning with the numerical current prediction layer. During the testing stage, the algorithm estimates the PV panel model parameters using the observed signals and can be applied in fault detection, health monitoring, and maximum power point tracking.