Intelligent Real-Time Photovoltaic Panel Monitoring System Using Artificial Neural Networks

Abstract

Photovoltaic panels system is becoming a popular choice as an alternative source of energy. This system comes with many challenges. To harness reliable energy efficiently, the photovoltaic panels system must remain in its best condition. This requires continuous maintenance and monitoring. However, in case of weather dependable energy yield change, and in order to identify if this change is normal due to environmental conditions, or is not normal because of faulty, or shaded, or dust-covered panel, an intelligent monitoring system is required. In this paper, we present a novel real-time monitoring system utilizing a small but efficient artificial neural network that is adequate to run on a low-cost system. The presented PV monitoring system can identify if the photovoltaic panel exhibit degradation due to fault conditions. In order to do that, the monitoring system implements an efficient small artificial neural network reference model. This artificial intelligent reference model is used to predict the output power of a normal operational photovoltaic panel under a set of changing environmental conditions. Moreover, the introduced monitoring system can monitor heterogamous PV panels with different manufacturing characteristics. In addition, the proposed monitoring system has the ability to log data online, over the internet, to facilitate other important features such as notification, system configuration update and further training, and data analysis. This paper discusses the different components of this novel artificial intelligent heterogeneous PV panels monitoring system.