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.
Highlights
To avoid any denial of service, power supplies for safety critical applications [1,2,3,4,5,6,7,8,9], need continuous monitoring to predict possible faults
The paper has proposed a scalable architecture for safety critical applicationswhere with predictive diagnosis capabilities
The aim is tosystem overcome the limitations of the state‐of‐the‐art predictive diagnosis capabilities
Summary
To avoid any denial of service, power supplies for safety critical applications (e.g., industrial automation, oil & gas, transport, defense) [1,2,3,4,5,6,7,8,9], need continuous monitoring to predict possible faults. Each cabinet contains multi-phase switching power converters (AC/DC, DC/DC, DC/AC, AC/AC), or power isolation transformers, or energy storage modules based on back-up battery units for UPS service. This work presents first a scalable architecture for UPS systems and to achieve predictive diagnosis capability, a distributed network of measuring and processing nodes.
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