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
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,...
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2024-01-01
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| author | Tanzim Jim Hassan Jamison Jangula Akshay Ram Ramchandra Niroop Sugunaraj Barathwaja Subash Chandar Prashanth Rajagopalan Farishta Rahman Prakash Ranganathan Ryan Adams |
| author_facet | Tanzim Jim Hassan Jamison Jangula Akshay Ram Ramchandra Niroop Sugunaraj Barathwaja Subash Chandar Prashanth Rajagopalan Farishta Rahman Prakash Ranganathan Ryan Adams |
| author_sort | Tanzim Jim Hassan |
| collection | DOAJ |
| description | 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 (<inline-formula> <tex-math notation="LaTeX">$69 kV_{AC}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$230 kV_{AC}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$345 kV_{AC}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$500 kV_{AC}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$250 kV_{DC}$ </tex-math></inline-formula>). 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. |
| format | Article |
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| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
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| spelling | doaj-art-733a5fc4af294b50acf2999785c1540f2025-01-04T00:00:45ZengIEEEIEEE Access2169-35362024-01-0112822898231710.1109/ACCESS.2024.339453210509673UAS-Guided Analysis of Electric and Magnetic Field Distribution in High-Voltage Transmission Lines (Tx) and Multi-Stage Hybrid Machine Learning Models for Battery Drain EstimationTanzim Jim Hassan0https://orcid.org/0009-0008-0982-9444Jamison Jangula1https://orcid.org/0009-0002-5173-7873Akshay Ram Ramchandra2Niroop Sugunaraj3https://orcid.org/0000-0002-6165-0862Barathwaja Subash Chandar4Prashanth Rajagopalan5Farishta Rahman6https://orcid.org/0009-0000-8934-2709Prakash Ranganathan7https://orcid.org/0000-0001-8638-660XRyan Adams8https://orcid.org/0000-0002-3206-9673School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USASchool of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USASchool of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USASchool of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USASchool of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USASchool of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USASchool of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USASchool of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USASchool of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USAUnmanned 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 (<inline-formula> <tex-math notation="LaTeX">$69 kV_{AC}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$230 kV_{AC}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$345 kV_{AC}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$500 kV_{AC}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$250 kV_{DC}$ </tex-math></inline-formula>). 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.https://ieeexplore.ieee.org/document/10509673/Batteryelectric fieldhybrid modelKNN regressormachine learningmagnetic field |
| spellingShingle | Tanzim Jim Hassan Jamison Jangula Akshay Ram Ramchandra Niroop Sugunaraj Barathwaja Subash Chandar Prashanth Rajagopalan Farishta Rahman Prakash Ranganathan Ryan Adams 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 IEEE Access Battery electric field hybrid model KNN regressor machine learning magnetic field |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Battery electric field hybrid model KNN regressor machine learning magnetic field |
| url | https://ieeexplore.ieee.org/document/10509673/ |
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