Incipient Offset Current Sensor Fault Detection and Diagnosis using Statistical Analysis and the Kullback Leibler Divergence for AC drive

Demba Diallo (1) , Claude Delpha (2)

1 GeePs - Laboratoire Génie électrique et électronique de Paris
2 L2S - Laboratoire des signaux et systèmes

In this paper, we propose line current sensor fault detection for AC drives. The method is based on the measured currents and the features are extracted either in the natural reference frame or in the transformed Park synchronous rotating frame. The features are the first four statistical moments or the Kullback Leibler Divergence (KLD) of the Probability Density Functions (PDF). For offset fault, we show that if the offset is higher than 3% of the current amplitude, the mean value is the most relevant value among the first four statistical moments that leads to good detection performances (low probability of false alarm and low probability of miss detection). But for incipient faults (offset ranging from 1 to 2%), even the projection in the transformed Park reference frame cannot improve the fault detection. For these cases, we show that the fault information can be retrieved using the PDF and the KLD. This is confirmed by the results showing that the fault is detected with 100% probability of detection.

Mots clés: Current sensor fault; Fault Detection; statistical moments; Kullback-Leibler Divergence

Domaines: Traitement du signal et de l'image [eess.SP]

Demba Diallo, Claude Delpha. Incipient Offset Current Sensor Fault Detection and Diagnosis using Statistical Analysis and the Kullback Leibler Divergence for AC drive. 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017), Oct 2017, Beijing, China. ⟨10.1109/iecon.2017.8217416⟩. ⟨hal-01578467⟩