Researchers at The Ohio State University have developed a machine learning model for predicting how susceptible overhead transmission lines are to damage when natural hazards like hurricanes or earthquakes happen in quick succession.
An essential facet of modern infrastructure, steel transmission towers help send electricity across long distances by keeping overhead power lines far off the ground. After severe damage, failures in these systems can disrupt networks for long periods.
The study, published in the journal Earthquake Engineering and Structural Dynamics, uses simulations to analyze what effect prior damage has on the performance of these towers once a second hazard strikes. Their findings suggest that previous damage has a considerable impact on the fragility and reliability of these networks if it can’t be repaired before the second hazard hits.
The machine learning model not only found that a combination of an earthquake and hurricane could be particularly devastating to an electrical grid, but that the order of the disasters may 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 the hurricane comes first and is followed by an earthquake.
After training the model for numerous scenarios, the research team created ‘fragility models’ that tested how the structures would hold up under different characteristics and intensities of natural threats. With the help of these simulations, researchers concluded that tower failures due to a single hazardous event were vastly different from the pattern of failures caused by multi-hazard events. The study noted that many of these failings occurred in the leg elements of the structure, a segment of the tower that helps bolt the structure to the ground and prevents collapse.
Overall the research shows a need to focus on re-evaluating the entire design philosophy of these networks. Yet to accomplish such a task, much more support from utilities and government agencies is needed.
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy of the Republic of Korea (MOTIE).