KETEP 세계에너지시장정보

Maintaining a stable power grid requires anticipating potential issues before they disrupt operations. Step-down transformers, crucial for delivering usable voltage levels to consumers, are susceptible to brownout conditions—a decrease in voltage that can affect equipment performance. 

This project used a decision tree classifier, a popular supervised learning algorithm. A large language model (LLM) was used to create the Python code to establish the decision tree classifier. Specifications for partial short circuit, short circuit, and brownout behaviors were included during the prompt engineering session. Google Colaboratory was used to execute the AI-generated Python code to reduce library resources and dependencies on a standard laptop computer. A description of a decision tree is provided next.