Artificial Intelligence to Cope Extreme Natural Events in Power System

Extreme natural events (ENE), as high-impact, low-probability events, may significantly affect the operation of a power system. ENE's effects, such as floods, hurricanes, wildfires, earthquakes, ice storms, and land sliding, are increasingly threatening the security and reliability of power networks. Climate change projections also indicate that such events' frequency and severity might increase in the future. Approximately 75% of power outages worldwide are directly caused by weather-inflicted incidents or indirectly by equipment deterioration due to weather exposure. Evaluating the ENE impacts on power systems is a complex problem due to the weather's stochastic, intermittent, and complex nature. Such studies go beyond the classic power system analysis and merge into metrology, geology, data science, statistics, etc. Artificial intelligence and statistical learning bring hopes to study power systems extreme events with data-driven approaches. This panel aims to bring together students, engineers, scientists, system operators, and regulators to discuss how the Artificial Intelligence decodes extreme natural events impacts on power system operation.