Internet of Things Platform for Photovoltaic Maintenance Management: Combination of Supervisory Control and Data Acquisition System and Aerial Thermal Images

Abstract

Suitable maintenance management plants of solar photovoltaic plants are required for global energy demands. The volume and variety of data acquired by thermographic cameras carried by unmanned aerial vehicles and Supervisory Control and Data Acquisition Systems increase the complexity of fault detection and diagnosis. The maintenance industry is requiring novel fault detection techniques that can be implemented in Internet of Thing platforms to automate the analysis and increase the suitability and reliability of the results. This paper presents a novel platform built with PHP, HTML, CSS and JavaScript for the combined analysis of data from Supervisory Control and Data Acquisition Systems and thermal images. The platform is designed. A real case study with thermal images and time series data from the same photovoltaic plant is presented to test the viability of the platform. The analysis of thermal images showed a 97% of accuracy for panel detection and 87% for hot spot detection. Shapelets algorithm is selected for time series analysis, providing an 84% of accuracy for the pattern selected by user. The platform has proven to be a flexible tool that can be applied for different solar plants through data upload by users.