UAS-Guided Analysis of Electric and Magnetic Field Distribution in High-Voltage Transmission Lines (Tx) and Multi-Stage Hybrid Machine Learning Models for Battery Drain Estimation

Abstract

Unmanned aerial systems/vehicles (UAS/UAVs) are increasingly utilized for inspecting high-voltage (HV) Tx lines. Operating on batteries, these UAVs are equipped with various electrical sensors, microprocessors, and motors. All of these sensors are susceptible to electric (E) and magnetic (H) fields, which may affect sensing, communication, and control operations of UAS. This paper is first-of-its-kind large scale study that gathered field data (E/H) across 5 different Tx lines ( $69 kV_{AC}$ , $230 kV_{AC}$ , $345 kV_{AC}$ , $500 kV_{AC}$ , and $250 kV_{DC}$ ). Additional flight data on a microwave tower was also gathered. Multiple types of DJI UAVs (M2EA, M30, and M300) are used to carry E/H field sensors, and radio frequency (RF) sensors as payload for this study. Specific measurements include the E field in V/m, H field in mG, Battery voltage in V, Battery current in A, Battery percentage, Battery Temperature in °F, latitude, and longitude. The overall goal of the paper is to quantify E/H field distribution in HVTx lines which aided in realization of FAA rule making on how close UAVs could fly safely. The preliminary findings indicate that significantly higher E/H field level were reported near AC than DC Tx lines. The paper discusses conditions influencing E/H field strength during UAV operations. Additionally, hybrid machine learning (HML) models (RFR-SVM, RFR-KNN) were used to forecast battery drain. The results indicate that the hybrid RFR-KNN model yields lower MAPE values compared to stand-alone and other hybrid models.

Keywords

• Battery
• electric field
• hybrid model
• KNN regressor
• machine learning
• magnetic field