Simulating a Lack of Phase Fault on SynRM and Integrate Results with Machine Learning
Hristo Milushev, Georgi Enchev, Nikolay Djagarov
Abstract
Modern intelligent systems for diagnostics of electric drives widely use the methods of artificial intelligence. In the article, a complex mathematical model of synchronous reluctance machine (SynRM) is proposed, with the help of which various fault can be simulated. With the help of the model, a lack of phase fault was simulated. Using a machine learning method, the fault was identified with an accuracy of over 97 percent.
Topics & Concepts
Fault (geology)Computer scienceControl engineeringMagnetic reluctancePhase (matter)Artificial intelligenceMachine learningEngineeringElectrical engineeringMagnetChemistrySeismologyOrganic chemistryGeologyIndustrial Engineering and TechnologiesElectric Power Systems and ControlEngineering Diagnostics and Reliability