Digital Twin Modeling for Photovoltaic Panels Based on Hybrid Neural Network

Abstract

An data-driven approach for Photovoltaic(PV) panel digital twin is proposed and practised in this paper. A hybrid neural network is applied to simulate PV power-voltage characteristics. Various environmental features such as uneven lighting conditions, temperature and humidity are selected and modified to manifest the physical mechanism to the utmost extent. Additionally a reduction method for lighting conditions as well as an improved genetic coding scheme are adopted to optimize the digital twin parameters. Above-mentioned data is acquired from an IoT management platform linked to specific PV panel and applied in the practice of digital twin modeling, performance of which is verified and discussed at the very end. The conclusions show that the digital twin is able to restore PV power-voltage characteristics under various lighting conditions as the average error of the testing simulation results is within 6%.