Date of Award

January 2025

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical Engineering

First Advisor

Prakash Ranganathan

Abstract

Unmanned aerial systems/vehicles (UAS/UAVs) are increasingly utilized for inspecting high-voltage (HV) transmission (Tx) lines. However, operating in the vicinity of electric (E) and magnetic (H) fields can affect UAV control and battery performance. Pilots unfamiliar with high E/H field environments often struggle to accurately estimate remaining flight time, resulting in either premature mission terminations or crashes due to depleted batteries. To address these challenges, this thesis focuses on developing an intelligent Battery Management System (BMS) for UAVs to ensure safe and reliable surveillance operations under various uncertain conditions, such as wind gusts, temperature variations, E/H fields, battery drain, and payload variability. A major limitation in this field has been the lack of ground-truth aerial E/H field data from HV Tx lines, which is critical for understanding field distributions and their impact on UAS electronics. To overcome this, a first-of-its-kind large-scale study was conducted, gathering real-time E/H field data across five different transmission lines (69 kVAC, 230 kVAC, 345 kVAC, 500 kVAC, and 250 kVDC), as well as a microwave tower. The data revealed that AC transmission lines exhibited significantly higher E/H field levels compared to DC lines. The study also investigated the influence of high E/H fields on UAV battery performance by measuring power drain under varying field intensities, operational scenarios, and environmental conditions. Analysis of the collected data uncovered critical correlations between battery behavior and proximity to high-voltage infrastructure, offering new insights into the effects of these environments on UAV operations. Building on these findings, a data-driven battery drain forecasting model was developed to enhance flight safety and operational efficiency. Various Hybrid Machine Learning (HML) models were compared, where the hybrid Random Forest-K-Nearest Neighbors (RF-KNN) model demonstrating the best performance by achieving the lowest Mean Absolute Percentage Error (MAPE) among all models tested. The findings of this research make significant contributions to UAV energy management, HV infrastructure inspection, and intelligent flight operation planning. The work not only enables safer and more reliable UAV applications in challenging environments but also aids in implementing FAA rule-making regarding safe operational proximity of UAVs to transmission lines.

Download

Share

COinS