A Fault Diagnostic Scheme for Predictive Maintenance of AC/DC Converters in MV/LV Substations

Giovanni Betta, Domenico Capriglione, Luigi Ferrigno, Marco Laracca, Gianfranco Miele, Nello Polese and Silvia Sangiovanni
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Giovanni Betta: Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy
Domenico Capriglione: Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy
Luigi Ferrigno: Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy
Marco Laracca: Department of Astronautics, Electrical and Energy Engineering, Sapienza University of Rome, 00184 Rome, Italy
Gianfranco Miele: Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy
Nello Polese: Faculty of Economics, Mercatorum University, 00186 Rome, Italy
Silvia Sangiovanni: Department of Astronautics, Electrical and Energy Engineering, Sapienza University of Rome, 00184 Rome, Italy

Energies, 2021, vol. 14, issue 22, 1-23

Abstract: The reliability of systems and components is a fundamental need for the efficient development of a smart distribution grid. In fact, the presence of a fault in one component of the grid could potentially lead to a service interruption and loss of profit. Since faults cannot be avoided, the introduction of a diagnostic scheme could predict the fault of a component in order to carry out predictive maintenance. In this framework, this paper proposes a novel Fault Detection and Isolation (FDI) scheme for AC/DC converters in MV/LV substations. In order to improve the reliability of the FDI procedure, the system architecture includes also an Instrument Fault Detection and Isolation section for identifying faults that could occur on the instruments and sensors involved in the monitoring process of the AC/DC converter. The proposed architecture is scalable, easily upgradable, and uses cost-effective sensors. Tests, carried out on a real test site, have demonstrated the efficacy of the proposal showing very good IFDI diagnostic performance for the 12 types of faults tested. Furthermore, as the FDI diagnostic performance regards, it shows a detection rate close to 100%.

Keywords: FDI; IFDI; predictive maintenance; AC/DC converter; fault diagnosis (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: 2021
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