TY - GEN
T1 - A novel Counter based Neural Network for Switching Fault Identification in Power Inverter
AU - Prathap, Joseph Anthony
AU - Modem, Shruthi
AU - Rafeeq Ahmed, K.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper proposes a novel neural network for the detection of switching faults in the power converters and inverters. The highlight of this work is the enhanced counter based neural network utilized for the identification of switching faults in the 27-level Ladder Multi-level Inverter. Though there are several machine and deep learning algorithms, the neural network algorithm present high reliability, accuracy and consistency in the classification, identification and prediction. In this work, the neural network is combined with the enhanced counter circuit to detect the fault occurrence in the inverter circuit. The proposed algorithm is developed using the VHDL code and synthesized in the FPGA board. The performance analysis is evaluated for power, area and IC layout for the proposed method.
AB - This paper proposes a novel neural network for the detection of switching faults in the power converters and inverters. The highlight of this work is the enhanced counter based neural network utilized for the identification of switching faults in the 27-level Ladder Multi-level Inverter. Though there are several machine and deep learning algorithms, the neural network algorithm present high reliability, accuracy and consistency in the classification, identification and prediction. In this work, the neural network is combined with the enhanced counter circuit to detect the fault occurrence in the inverter circuit. The proposed algorithm is developed using the VHDL code and synthesized in the FPGA board. The performance analysis is evaluated for power, area and IC layout for the proposed method.
KW - fault identification
KW - field programmable gate array
KW - ladder inverter
KW - neural network
UR - https://www.scopus.com/pages/publications/85191727249
U2 - 10.1109/ICPEA60617.2024.10499123
DO - 10.1109/ICPEA60617.2024.10499123
M3 - Conference contribution
AN - SCOPUS:85191727249
T3 - 2024 IEEE 4th International Conference in Power Engineering Applications: Powering the Future: Innovations for Sustainable Development, ICPEA 2024
SP - 37
EP - 41
BT - 2024 IEEE 4th International Conference in Power Engineering Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference in Power Engineering Applications, ICPEA 2024
Y2 - 4 March 2024 through 5 March 2024
ER -