An Evolutionary EMI Filter Design Approach Based on In-Circuit Insertion Loss and Optimization of Power Density

Massimiliano Luna, Giuseppe La Tona, Angelo Accetta, Marcello Pucci and Maria Carmela Di Piazza
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Massimiliano Luna: Istituto di Ingegneria del Mare (INM), Consiglio Nazionale delle Ricerche (CNR), 90146 Palermo, Italy
Giuseppe La Tona: Istituto di Ingegneria del Mare (INM), Consiglio Nazionale delle Ricerche (CNR), 90146 Palermo, Italy
Angelo Accetta: Istituto di Ingegneria del Mare (INM), Consiglio Nazionale delle Ricerche (CNR), 90146 Palermo, Italy
Marcello Pucci: Istituto di Ingegneria del Mare (INM), Consiglio Nazionale delle Ricerche (CNR), 90146 Palermo, Italy
Maria Carmela Di Piazza: Istituto di Ingegneria del Mare (INM), Consiglio Nazionale delle Ricerche (CNR), 90146 Palermo, Italy

Energies, 2020, vol. 13, issue 8, 1-21

Abstract: Power density is one of the most significant issues in designing electromagnetic interference (EMI) filters for power electronic-based applications. Therefore, an effective EMI filter design should consider both its capability to ensure the compliance with the related EMI standard limits and the possibility to build it by suitable components leading to the most compact configuration as well. To fulfill the above requirements, in this paper, an automatic procedure to get an improved design of EMI filters is proposed. Specifically, according to the proposed method, the values of filter parameters for both common mode (CM) and differential mode (DM) sections are selected by a genetic algorithm (GA) exploiting the in-circuit insertion loss, thus obtaining a more effective design. Besides, the components that set up the filter are selected by a rule-based procedure searching through a suitable database of commercial components to identify those allowing for the maximum power density. Experimental tests were performed using an inverter-fed induction motor drive as a case study, and the obtained results have demonstrated the validity of the proposed approach.

Keywords: EMI filter; power converter; power density; optimal design; genetic algorithm; electrical drives (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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