DYNA JOURNAL ENGINEERING

Key words:

digital twins, DT, inspection and maintenance, wind farms, UAVs, IoT, RTSP, MQTT, asset monitoring, predictive maintenance, artificial intelligence, fault detection, remote sensing, autonomous flight, real-time analytics, cyber-physical systems, API integration, virtual environment.

Article type:

ARTICULO DE INVESTIGACION / RESEARCH ARTICLE

Section:

RESEARCH ARTICLES

The inspection and maintenance of wind farms are inherently complex and costly due to the considerable number of turbines requiring continuous monitoring, the logistical challenges related to site accessibility, and the need for specialized inspection equipment. To address these challenges and reduce associated costs, emerging technologies such as Digital Twins (DTs) and Unmanned Aerial Vehicles (UAVs) are increasingly being adopted. The integration of UAVs with DTs in wind farm infrastructure enables continuous and remote monitoring of assets, structures, and environmental conditions, enhancing operational efficiency, optimizing maintenance strategies, enabling full digitization, and significantly reducing maintenance overhead. This work presents a comprehensive methodology for the design and development of a custom-built UAV, along with the implementation of an Early-Stage Digital Twin (ESDT) that takes advantage of the use of the data extracted from high-resolution cameras embedded in UAVs, to enable the detection of potential faults in wind farm assets, facilitating timely, data-driven interventions. Additionally, a hybrid video streaming approach is introduced, utilizing both RTSP and MQTT protocols to support real-time inspection, tracking, and fault identification. Finally, a fully operational digital platform is demonstrated, capable of seamless integration with UAVs during re-al-world flight missions, laying the groundwork for remote control and autonomous UAV navigation, by contextualizing real-time drone inspection video with flight tracking in a virtual wind farm environment. Moreover, the ESDT ‘s architecture is prepared to support endpoints and API processes that can apply additional predictive and corrective algorithms to the video stream or to future numerical sensor data, adding more functionalities and increasing its capabilities towards DT standards.
Key Words: digital twins (DT), inspection and maintenance, wind farms, UAVs, IoT, RTSP, MQTT