Enhancing power grid resilience with cutting-edge subsurface sensing technology

HOUSTON, Dec. 03, 2024 – When Hurricane Beryl hit Houston in July, it caused extensive damage to trees and utility poles, leaving over two million households without power. A week later, 250,000 Texans still faced the extreme summer heat without electricity or air conditioning, and at least three people tragically lost their lives due to heat exposure. This disaster spotlighted critical vulnerabilities in Houston’s largely above-ground power grid, leading to much analysis. 

One key reason Houston, and the U.S. in general, has a vulnerable power grid is the reliance on overhead power lines rather than underground ones. According to the Edison Electric Institute, underground power lines are 8–10 times more reliable than their overhead counterparts. However, less than 20% of power lines in the U.S. are buried, a significantly lower percentage compared to other developed nations, such as France (40%), Germany (70%), and the Netherlands (90%). The primary obstacle to burying power lines is the high cost, which can be 5–10 times greater than that of overhead lines. Additionally, current undergrounding methods, such as trenching, raise safety concerns, including the potential for damaging other buried utilities during installation, as well as prolonged surface disruptions and traffic detours that can impact the safety and convenience of local communities. These challenges make undergrounding a complex and costly undertaking.

Recently, the U.S. Department of Energy launched an ARPA-E Program called GOPHURRS, which stands for Grid Overhaul with Proactive, High-speed Undergrounding for Reliability, Resilience, and Security, and has allocated $34 million for 12 projects across 11 states to strengthen and modernize America’s aging power grid by spurring the development of cost-effective, high-speed, and safe undergrounding technologies.

“Modernizing our nation’s power grid is essential to building a clean energy future that lowers energy costs for working Americans and strengthens our national security,” said U.S. Secretary of Energy Jennifer M. Granholm in a DOE press release.

One of the projects selected, awarded $3.3 million in funding, is the “Artificial Intelligence and Unmanned Aerial Vehicle Real-Time Advanced Look-Ahead Subsurface Sensor,” which brings together the technological expertise of Hawaii-based Oceanit with the Tier-One research capabilities of the University of Houston.

“If proven successful, our proposed look-ahead subsurface sensing system could significantly reduce the costs of horizontal directional drilling for installing underground utilities. Promoting HDD offers environmental advantages over traditional trenching methods and enhances the power grid’s resilience.” -- Jiefu Chen, associate professor of electrical and computer engineering at UHassociate professor of electrical and computer engineering at UH.

The team is working to develop a state-of-the-art subsurface sensing system to guide safe and efficient underground power line installation. The aim is to create a real-time, high-resolution look-ahead sensing system using unmanned aerial vehicles, electromagnetic resistivity well logging, and machine learning. This technology will detect underground obstacles in front of a drill bit, minimizing damage to existing infrastructures and enabling a smoother installation process.

“Advanced subsurface sensing and characterization technologies are essential for the undergrounding of power lines,” said Jiefu Chen, associate professor of electrical and computer engineering at UH who is a key collaborator on the project. “This initiative can enhance the grid's resilience against natural hazards such as wildfires and hurricanes.”

The end goal is to produce a prototype capable of generating near real-time, high-resolution underground images during horizontal directional drilling or HDD.

On the UH team, Chen focuses on designing electromagnetic antennas installed on UAV and HDD drilling string, as well as optimization of the subsurface imaging system; Yueqin Huang, assistant professor of information science technology, leads the geophysical signal processing needed to construct precise subsurface images ahead of the drill bit, while Xuqing Wu, associate professor of computer information systems, integrates machine learning for faster modeling and real-time image generation.

“If proven successful, our proposed look-ahead subsurface sensing system could significantly reduce the costs of horizontal directional drilling for installing underground utilities,” said Chen. “Promoting HDD offers environmental advantages over traditional trenching methods and enhances the power grid’s resilience.”