TY - GEN
T1 - Machine learning based fault classification approach for power electronic converters
AU - Bharath Kurukuru, V. S.
AU - Haque, Ahteshamul
AU - Kumar, Rajesh
AU - Khan, Mohammed Ali
AU - Tripathy, Arun Kumar
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/16
Y1 - 2020/12/16
N2 - This research proposes a fault classification approach for power electronics converters (PECs) operating with the distributed generation systems. The proposed approach classifies and localizes the faults in PECs by adapting wavelet transforms and artificial neural networks. Initially, various failure mechanisms for power modules in PECs are identified to generate the fault data. Further, the detailed and approximate coefficients of the fault data at each frequency band are extracted using wavelet transform and used as inputs to the classifier. The ANN classifier estimates the non-linear relationship between the features and targets patterns to develop the fault classification mechanism. The numerical simulations are carried out for injecting various faults and degradation scenarios in both the legs of the inverter and developing the fault classifier in Plecs/MATLAB integration. The results showed 97.4% training accuracy with component failure classifier and 94.2% training accuracy with the classifier trained for 50% degradation in power module.
AB - This research proposes a fault classification approach for power electronics converters (PECs) operating with the distributed generation systems. The proposed approach classifies and localizes the faults in PECs by adapting wavelet transforms and artificial neural networks. Initially, various failure mechanisms for power modules in PECs are identified to generate the fault data. Further, the detailed and approximate coefficients of the fault data at each frequency band are extracted using wavelet transform and used as inputs to the classifier. The ANN classifier estimates the non-linear relationship between the features and targets patterns to develop the fault classification mechanism. The numerical simulations are carried out for injecting various faults and degradation scenarios in both the legs of the inverter and developing the fault classifier in Plecs/MATLAB integration. The results showed 97.4% training accuracy with component failure classifier and 94.2% training accuracy with the classifier trained for 50% degradation in power module.
KW - Artificial neural network (ANN)
KW - Distributed generation
KW - Fault classification
KW - Power electronics converter (PEC)
KW - Wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=85103912279&partnerID=8YFLogxK
U2 - 10.1109/PEDES49360.2020.9379365
DO - 10.1109/PEDES49360.2020.9379365
M3 - Conference contribution
AN - SCOPUS:85103912279
T3 - 9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020
BT - 9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020
Y2 - 16 December 2020 through 19 December 2020
ER -