It is the average distance a charge carrier travels after electron-hole pair generation but before recombination.
Published in Chapter:
A Deep Learning-Based Solar Photovoltaic Emulator
Gurunandh V. Swaminathan (CEG, Anna University, Chennai, India) and Somasundaram Periasamy (CEG, Anna University, Chennai, India)
Copyright: © 2023
|Pages: 20
DOI: 10.4018/978-1-6684-8816-4.ch009
Abstract
With solar photovoltaic (PV)-based power generation becoming popular, research is being carried out in multiple facets of the PV system. The electrical aspect is one such area where objectives are extracting maximum power, developing power management strategies, etc. Hence, while experimenting with new strategies it is desirable to have controllable irradiation and temperature conditions. This ensures that external factors are constant for comparing the effectiveness of different strategies. Thus, PV emulators are a suitable solution to this. A deep learning-based PV emulator is proposed where the proposed PV emulator is capable of emulating a PV panel under uniform and non-uniform irradiance conditions. A deep learning network is modeled with irradiance, temperature, ratio of partial shading, PV current as inputs, & PV voltage as output. Then, a buck converter is controlled to follow the reference voltage, thereby mimicking the desired I-V characteristics. The deep learning model was trained and built in Python, and simulations to validate the performance were carried out in Simulink.