Model-Based Detection and Estimation of DC Offset of Phase Current Sensors for Field Oriented PMSM Drives

Failure of a phase current sensor in permanent magnet synchronous machine (PMSM) drive can highly impact the performance of the drive. Especially, offset faults can cause torque oscillations, which can lead to an unacceptable deterioration of the drive's performance and mechanical damages. The...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2023-06, Vol.70 (6), p.6316-6325
Main Authors: Attaianese, Ciro, D'Arpino, Matilde, Monaco, Mauro Di, Noia, Luigi Pio Di
Format: Article
Language:English
Subjects:
Algorithms
Circuit faults
Current measurement
Electric drives
Exact solutions
Fault diagnosis
field oriented control (FOC)
model-based estimation
Observers
permanent magnet synchronous machine (PMSM) drive
Permanent magnets
Phase current
Q measurement
sensor fault
Sensors
State estimation
State observers
Stators
Synchronous machines
Voltage measurement
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Description
Summary:Failure of a phase current sensor in permanent magnet synchronous machine (PMSM) drive can highly impact the performance of the drive. Especially, offset faults can cause torque oscillations, which can lead to an unacceptable deterioration of the drive's performance and mechanical damages. The diagnosis of this fault condition is of fundamental importance in many applications and it is often solved by means of highly computational state estimation techniques. This article presents a new model-based approach to systematically and accurately detect and estimate phase current sensor offsets in PMSM drives. The steady-state analytical solution of the PMSM drive model, including the control loop of the field oriented control and the sensor offset disturbance, has been found, allowing for estimating the effect of the fault on the electric drive performance. Then, a simple and effective diagnostic algorithm has been developed to detect, isolate, and accurately estimate the location and the magnitude of the fault eliminating any need for state estimation or observers. Numerical and experimental results have been reported to validate the proposed model and diagnostic algorithm even in case of motor parameters variation.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2022.3192683