Study uses artificial intelligence to predict fragility of electric grid networks – ScienceDaily

A single natural disaster could disrupt electrical service for millions. A new study suggests that cascading disasters can cause catastrophic damage, but the research also identifies new ways to monitor and maintain power grids.

Researchers at Ohio State University have developed a machine-learning model to predict how vulnerable overhead transmission lines will be when natural hazards such as hurricanes or earthquakes occur in quick succession.

An essential aspect of modern infrastructure, steel transmission towers help send electricity over long distances by keeping overhead power lines off the ground. After severe damage, malfunctions in these systems can disrupt networks across affected communities, and it takes anywhere from a few weeks to months to fix.

The study, published in the journal Earthquake Engineering and Structural Dynamics, uses simulations to analyze the effect of past damage on the performance of these towers once a second hazard has occurred. Abdullah Shafizadeh, co-author of the study and associate professor of civil and environmental engineering, said their findings indicate that past damages have a significant impact on the fragility and reliability of these networks if they are not repaired before the second hazard occurs in geodetic engineering.

“Our work aims to answer whether systems can be designed and managed in a way that not only reduces their initial damage but enables them to recover faster,” Shafisadeh said.

The machine learning model didn’t find that a combination of an earthquake and a hurricane could be particularly devastating to the electrical grid, but the order of the disasters could make a difference. The researchers found that the probability of a tower collapse is much higher in the event of an earthquake followed by a hurricane than the probability of failure when a hurricane comes first and is followed by an earthquake.

This means that while communities will certainly suffer some setbacks in the event that a hurricane precedes an earthquake, a situation in which an earthquake precedes a hurricane can destroy the area’s electricity grid. These conclusions are why Chavisa’s research has significant implications for disaster recovery efforts.

“When power grid systems are widely spread over large geographical areas, not every inch of them can be examined very carefully,” Shafisada said. “Predictive models can help engineers or organizations know which towers have the greatest potential to fail and move quickly to improve those issues in the field.”

After training the model for several scenarios, the team created “fragility models” that tested how structures could hold up under different properties and densities of natural threats. With the help of these simulations, the researchers concluded that the failures of the towers due to a single hazardous event were quite different from the failure pattern caused by multi-hazard events. The study indicated that many of these failures occurred in the elements of the legs of the structure, which is a part of the tower that helps stabilize the structure with the ground and prevents collapse.

Overall, Shafizadeh said his research shows the need to focus on reassessing the entire design philosophy of these networks. However, to accomplish such a task, more support from government facilities and agencies is needed.

“Our work will be of great help in setting up new infrastructure systems in this field,” Shafisa said. “Together with our other research, this shows that we can dramatically improve the performance of the entire system using the same amount of resources we spend today, just by optimizing their allocation.”

This work was supported by the Korea Institute for Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy of the Republic of Korea (MOTIE).

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Materials provided by Ohio State University. Original by Tatiana Woodall. Note: Content can be modified according to style and length.

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