Backpropagation algorithm-based approach to mitigate soiling from PV module

Abstract

Energy is viewed as a prime operator in the era of riches and a noteworthy figure of financial advancement. Constrained fossil assets and ecological issues connected with them have underscored the requirement for new manageable energy supply alternatives that utilize renewable energies. Among accessible innovations for vitality generation from sun-based source, photovoltaic framework could give a huge commitment to build up a more sustainable vitality framework. Utilization of solar energy has not been opened up since the oil industry does not possess the sun. The solar PV modules are for the most part utilized in dusty situations which is the situation in subtropical nations like India. Dust gets gathered in the front surface of the module and hinders the passage of light from the sun. It diminishes the power era limit of the module. The power yield decreases as by half if the module is not cleaned. Keeping in mind the end goal to routinely clean the dirt, a programmed cleaning framework has been designed that both detects the dirt on solar panels and cleans the surface of solar module consequently. The system consists of a panel with wiper and water ejector. Artificial neural network (ANN) sends the signal to the wiper motor with the help of measured data of solar irradiance, panel temperature, PV voltage, current, and power in both sunny/cloudy as well as dusty days. These data were well trained and tested along with the unknown data which were not involved in the training. Backpropagation (BP) algorithm was used to train the network, which showed the accuracy of 99% in data prediction and accordingly, generated the control signal for windshield wiper motor and wiped the dust.