Auto-adaptive and Dynamical Clustering for Open-Circuit Fault Diagnosis of Power Inverters

Thanh Hung Pham (1) , Sanda Lefteriu (1) , Cécile Labarre (1) , Eric Duviella (1) , Stéphane Lecoeuche

1 IMT Lille Douai - Ecole nationale supérieure Mines-Télécom Lille Douai

This paper presents a fault diagnosis approach for single open-circuit faults in inverters entirely from measurements of the stator currents. These measurements are used to extract the feature data; the feature data is then used to create clusters in an online, adaptive and unsupervised way. Auto-adaptive and Dynamical Clustering (AUDyC) is the algorithm employed for this step. Based on the derived clusters, appropriate formulations for the data labelling and fault detection and isolation are proposed. The effectiveness of the approach is validated on simulation and experimental data.

Domaines: Sciences de l'ingénieur [physics]; Automatique / Robotique

Thanh Hung Pham, Sanda Lefteriu, Cécile Labarre, Eric Duviella, Stéphane Lecoeuche. Auto-adaptive and Dynamical Clustering for Open-Circuit Fault Diagnosis of Power Inverters. ECC 2019 - European Control Conference, Jun 2019, Naples, Italy. ⟨hal-02123169v2⟩