One natural disaster can disable millions of electricity. A new study suggests that back-to-back disasters can cause catastrophic damage, but the research also identifies new ways to monitor and maintain power grids.
Ohio State University researchers have developed a machine learning model to predict how susceptible overhead transmission lines are to damage when natural disasters such as hurricanes or earthquakes occur in rapid succession.
An essential facet of modern infrastructure, steel cell towers help transport electricity over long distances by keeping overhead power lines far from the ground. After severe damage, failures in these systems can disrupt networks in the affected communities, with recovery taking anywhere from weeks to months.
The study, published in the journal Earthquake Engineering and Structural Dynamics, uses simulations to analyze the effect previous damage has on the performance of these towers once a second threat strikes. Their findings suggest that past damage has a significant impact on the fragility and reliability of these networks if it cannot be repaired before the second danger hits, said Abdollah Shafieezadeh, co-author of the study and an associate professor of civil, environmental and geodesics. Technic.
“Our work is focused on answering the question of whether it is possible to design and manage systems to not only minimize initial damage, but also allow them to recover faster,” said Shafieezadeh.
The machine learning model found not only that a combination of an earthquake and hurricane can be particularly devastating to the power grid, but that the order of the disasters can make a difference. The researchers found that the probability of a tower collapsing is much higher in the case of an earthquake followed by a hurricane than the probability of failure when the hurricane comes first and is followed by an earthquake.
That means that while communities would certainly experience some setbacks in the event that a hurricane precedes an earthquake, a situation where an earthquake precedes a hurricane could devastate a region’s electrical grid. Such conclusions are why Shafieezadeh’s research has major implications for disaster recovery efforts.
“If large-scale power grids are spread over large geographic areas, it’s not possible to carefully inspect every inch of them,” Shafieezadeh says. “Predictive models can help engineers or organizations see which towers have the highest probability of failure and act quickly to improve those problems in the field.”
After training the model for numerous scenarios, the team created “fragility models” that tested how the structures would hold up under different characteristics and intensities of natural threats. Using these simulations, researchers concluded that tower failures due to a single hazard event were vastly different from the pattern of failures caused by multiple hazard events. The study noted that many of these deficiencies were in the structure’s leg members, a segment of the tower that helps bolt the structure to the ground and prevent collapse.
Overall, Shafieezadeh said his research shows the need to re-evaluate the entire design philosophy of these networks. But accomplishing such a task requires much more support from utilities and government agencies.
“Our work would be very helpful in creating new infrastructure regulations in the field,” Shafieezadeh said. “This along with our other research shows that we can significantly improve system-wide performance with the same amount of resources we spend today, simply by optimizing their allocation.”
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Commerce, Industry and Energy of the Republic of Korea (MOTIE).