Title :
Machine learning approaches to power-system security assessment
Author_Institution :
Dept. of Electr. Eng., Liege Univ., Belgium
Abstract :
The paper discusses a framework that uses machine learning and other automatic-learning methods to assess power-system security. The framework exploits simulation models in parallel to screen diverse simulation scenarios of a system, yielding a large database. Using data mining techniques, the framework extracts synthetic information about the simulated system´s main features from this database
Keywords :
deductive databases; digital simulation; expert systems; knowledge acquisition; learning (artificial intelligence); power system analysis computing; power system security; very large databases; automatic-learning methods; data mining; expert systems; large database; machine learning; power-system security assessment; simulation models; Data mining; Data security; Decision making; Information security; Machine learning; Numerical simulation; Power system analysis computing; Power system planning; Power system security; Power system simulation;
Journal_Title :
IEEE Expert
