Modelling of Indoor Light Energy Harvesting for IoT

Abstract

Network communications have been evolving with the rise of the Internet of Things (IoT). Low powered IoT devices, which consume less power, can be powered up using an energy harvesting system instead of batteries. Due to the wide availability of indoor lighting in offices, homes, factories, malls and hospitals, energy can be harvested through these sources. A photovoltaic panel that converts light energy into electrical energy is used to harvest the power from these indoor lighting. However, most manufacturers provide datasheet values only at limited values of illuminance. Thus, it is important to have an accurate model of photovoltaic panel that can predict and estimate its performance at all operating conditions. This paper presents the methodology for modelling an accurate Single Mechanism Five Parameter (1M5P) model for indoor light energy harvesting. The accuracy of the model is highly dependent on the parameter extraction technique used. A high accuracy technique for outdoor was adapted for the indoor application. An experiment was also undertaken to determine the equivalent irradiance for different values of illuminance. The proposed model was applied on a commercial PV panel and its I V and P-V curves were obtained. Then, a controlled experiment is carried out with the PV panel under the indoor light condition. The results for both the model and experiment were compared, and they were found to be in good agreement, thus validating the accuracy of the proposed model.