TY - GEN
T1 - Current Sensorless Model Predictive Control of Matrix Converter With Zero Common-Mode Voltage
AU - Sarajian, Ali
AU - Guan, Quanxue
AU - Wheeler, Patrick
AU - Khaburi, Davood Arab
AU - Kennel, Ralph
AU - Rodriquez, Jose
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - To eliminate the common-mode voltage (CMV) for matrix converters, this paper proposes a current sensorless model predictive control with reduced calculation overhead. In contrast to other traditional CMV-reducing methods which use all permissible switching configurations, this method synthesizes the output voltage and the input current with only six rotating vectors that lead to zero CMV. The proposed technique does not need to predict future load currents and source currents for those six rotating vectors, which provides another advantage in term of computation efficiency. Additionally, all current sensors are removed by using a Luenberger state observer instead in the control loop for cost reduction. The effectiveness of the proposed method is evaluated through simulation in different operation conditions.
AB - To eliminate the common-mode voltage (CMV) for matrix converters, this paper proposes a current sensorless model predictive control with reduced calculation overhead. In contrast to other traditional CMV-reducing methods which use all permissible switching configurations, this method synthesizes the output voltage and the input current with only six rotating vectors that lead to zero CMV. The proposed technique does not need to predict future load currents and source currents for those six rotating vectors, which provides another advantage in term of computation efficiency. Additionally, all current sensors are removed by using a Luenberger state observer instead in the control loop for cost reduction. The effectiveness of the proposed method is evaluated through simulation in different operation conditions.
KW - Luenberger observer
KW - Matrix converter
KW - model predictive control
UR - http://www.scopus.com/inward/record.url?scp=85143911303&partnerID=8YFLogxK
U2 - 10.1109/IECON49645.2022.9968351
DO - 10.1109/IECON49645.2022.9968351
M3 - Conference contribution
AN - SCOPUS:85143911303
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE Computer Society
T2 - 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
Y2 - 17 October 2022 through 20 October 2022
ER -