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A New Approach for Real-Time Multiple Open-Circuit Fault Diagnosis in Voltage-Source Inverters

Estima, Jorge O., Marques Cardoso, Antonio J.·2011·IEEE Transactions on Industry Applications

Abstract

Practically all the diagnostic methods for opencircuit faults in voltage-source inverters (VSI) developed during the last decades are focused on the occurrence of single faults and do not have the capability to handle and identify multiple failures. This paper presents a new method for real-time diagnostics of multiple open-circuit faults in VSI feeding ac machines. In contrast with the majority of the methods found in the literature which are based on the motor phase currents average values, the average absolute values are used here as principal quantities to formulate the diagnostic variables. These prove to be more robust against the issue of false alarms, carrying also information about multiple open-circuit failures. Furthermore, by the combination of these variables with the machine phase currents average values, it is possible to obtain characteristic signatures, which allow for the detection and identification of single and multiple open-circuit faults.

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Metadata

Authors
Estima, J. O., & Marques Cardoso, A. J.
Venue
IEEE Transactions on Industry Applications
Published
2011-11
Volume
47
Issue
6
Pages
2487-2494
ISSN
0093-9994 (ISSN-L)
OpenAlex
W1966336208
Cited by
298
References
1
FWCI
27.58
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A New Approach for Real-Time Multiple Open-Circuit Fault Diagnosis in Voltage-Source Inverters. https://app.cochat.ai/papers/10.1109/tia.2011.2168800
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Estima, J. O., & Marques Cardoso, A. J. (2011). A New Approach for Real-Time Multiple Open-Circuit Fault Diagnosis in Voltage-Source Inverters. IEEE Transactions on Industry Applications. https://doi.org/10.1109/tia.2011.2168800

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.