A Drone-Deployable Remote Current Sensor for Non-Invasive Overhead Transmission Lines Monitoring
The remote monitoring of current flow in overhead transmission cables is recognized as a significant challenge, yet reliable access to these measurements is regarded as essential for ensuring grid stability and system reliability. Accordingly, this paper introduces an innovative approach to remote current flow measurement in overhead Transmission Lines (TLs). The proposed solution features a drone-deployable sensor box with uniquely designed internal signal processing units that filter noise and safeguard signals before forwarding them to an Arduino Due. The Arduino Due processes the signals and transmits the Root Mean Square (RMS) current values to a remote computer via an HC-06 Bluetooth module. On the remote computer, equipped with a Bluetooth range extender, MATLAB is used to generate timestamped curves of the received data. Laboratory tests demonstrated a maximum average error of 5.22% under extreme conditions (e.g., Total Harmonic Distortions (THD) of 40% in the injected current signals) and a minimum error of 2.54% under standard TL operating conditions. The system supports current measurements up to 5 kA RMS and allows for 6 hours of continuous second-wise data recording on a single battery charge. The communication between the sensor box and MATLAB remained stable within a 60-meter radius in open air and 15 meters in a laboratory setting with physical barriers. This topology offers a promising solution for remote current monitoring in TLs, contributing to smart grid applications. The paper also outlines potential future work to enhance the system’s performance and applicability.
Authors
- Detlef Schulz (ORCID: https://orcid.org/0000-0002-3343-8653)
- Khaled Osmani (ORCID: https://orcid.org/0000-0003-4667-9664)
Institutions
- Helmut Schmidt University (DE)
Publication Details
- Journal
- IEEE Access
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/access.2025.3554570
- Citations
- 5
- Primary Topic
- Power Line Inspection Robots
- Type
- article
- Field-Weighted Citation Impact
- 3.57