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Doctoral Student

Leyla Shojaeifard

Automatically retrofitting variable-frequency drives using software solutions

The research aims to find ways to support the retrofitting of electric motor drives through technological solutions. One objective is to improve currently used behavioral models of electric drives. Another is to develop an AI/ML solution for optimizing drive parameter configurations. Machine Learning is expected to improve the efficiency of retrofitting efforts in the application domain by automating the search for optimal configuration parameters. Ideally, the developed solution could completely replace the manual work of tweaking the parameters. The results of this research may inspire future research to apply similar solutions to enhance efficiency in different application domains.

Industry partner
Juha Kuusela

Danfoss

The research aims to find ways to support the retrofitting of electric motor drives through technological solutions. One objective is to improve currently used behavioral models of electric drives. Another is to develop an AI/ML solution for optimizing drive parameter configurations. Machine Learning is expected to improve the efficiency of retrofitting efforts in the application domain by automating the search for optimal configuration parameters. Ideally, the developed solution could completely replace the manual work of tweaking the parameters. The results of this research may inspire future research to apply similar solutions to enhance efficiency in different application domains.

Academic supervisor
Tomi Männistö
Kari Systä
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