| Issue |
E3S Web Conf.
Volume 409, 2023
International Conference on Management Science and Engineering Management (ICMSEM 2023)
|
|
|---|---|---|
| Article Number | 04014 | |
| Number of page(s) | 10 | |
| Section | Project Management | |
| DOI | https://doi.org/10.1051/e3sconf/202340904014 | |
| Published online | 01 August 2023 | |
Internet of Things Platform for Photovoltaic Maintenance Management: Combination of Supervisory Control and Data Acquisition System and Aerial Thermal Images
Ingenium Research Group, Universidad Castilla-La Mancha, 13071 Ciudad Real, Spain
* e-mail: isaac.segovia@uclm.es
** e-mail: faustopedro.garcia@uclm.es
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.
Key words: Maintenance management / Machine learning / Photovoltaic / IoT platform
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.