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Energies 2016, 9(5), 327; doi:10.3390/en9050327

Distributed Measuring System for Predictive Diagnosis of Uninterruptible Power Supplies in Safety-Critical Applications

Dipartimento Ingegneria della Informazione-Università di Pisa, via G. Caruso 16, Pisa 56122, Italy
Academic Editor: Rodolfo Araneo
Received: 7 January 2016 / Revised: 2 April 2016 / Accepted: 21 April 2016 / Published: 28 April 2016
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Abstract

This work proposes a scalable architecture of an Uninterruptible Power Supply (UPS) system, with predictive diagnosis capabilities, for safety critical applications. A Failure Mode and Effect Analysis (FMEA) has identified the faults occurring in the energy storage unit, based on Valve Regulated Lead-Acid batteries, and in the 3-phase high power transformers, used in switching converters and for power isolation, as the main bottlenecks for power system reliability. To address these issues, a distributed network of measuring nodes is proposed, where vibration-based mechanical stress diagnosis is implemented together with electrical (voltage, current, impedance) and thermal degradation analysis. Power system degradation is tracked through multi-channel measuring nodes with integrated digital signal processing in the transformed frequency domain, from 0.1 Hz to 1 kHz. Experimental measurements on real power systems for safety-critical applications validate the diagnostic unit. View Full-Text
Keywords: uninterruptible power supply (UPS); predictive maintenance; measurements on power transformers; battery monitoring; power electronics and components uninterruptible power supply (UPS); predictive maintenance; measurements on power transformers; battery monitoring; power electronics and components
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Saponara, S. Distributed Measuring System for Predictive Diagnosis of Uninterruptible Power Supplies in Safety-Critical Applications. Energies 2016, 9, 327.

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