A New ANN-Based Cleaning Approach for Photovoltaic Solar Panels

Abstract

Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on reducing the maintenance costs by initiating cleaning actions when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an Artificial Neural Network (ANN). Experiments are conducted to collect the required data which are used in training the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. A sample case study shows that the proposed approach can schedule the cleaning actions to reduce the energy loss due to dust by 60% while saving 80% of the maintenance cost compared to daily cleaning.