Drone-aided electrical current sensing - a breakthrough for smart grids

This paper serves as an overview of the current progress concerning the DNeD (Digitalized, legally safe and low-emission airborNe inspection and grid data acquisition using automated Drones) project. DNeD aims to provide a full solution of an intelligent current sensor, integrable inside a dedicated drone-embedded sensor box. The research began with a brief survey of non-invasive current sensing technologies as a guide for the selection of the optimum current sensing physics. Afterwards, an all-optical sensor (MICATU RG235) was tested under laboratory conditions. The results from these tests were systematically tabulated. Concurrently, a study on magnetic field-based current sensing was performed using Finite Element Analysis (FEA) in COMSOL. This dual approach allows for a comprehensive evaluation of different sensing technologies. Ultimately, the study concludes that magnetic-based sensing, utilizing Hall effect sensors (e.g. DRV5055), is by far the most suitable approach. Additionally, the design of the sensor-box housing considered crucial mechanical factors such as robustness and weight. The design process utilized CAD models developed in SolidWorks, which form the backbone for real sensor-box manufacturing. Overall, the circuitry suggestions consisted of the development of analogue modulation electronic circuits, incorporating low pass filters, voltage dividers, and additional protective circuitry to ensure the reliability and accuracy of the sensor data. A prototype was developed, featuring Bluetooth as the communication protocol between the sensor-box and a remote system running MATLAB. Preliminary results indicate that the prototype effectively transfers dummy analogue voltage signals over distances up to 30 metres, demonstrating the feasibility of the design for practical applications. This comprehensive study not only addresses the mechanical and physical design aspects of embedding an intelligent sensor in a drone but also explores various current sensing technologies and their practical implementation. The findings provide a robust framework for future development and optimization of intelligent sensor systems in aerial applications.