TY - JOUR
T1 - A data-driven based voltage control strategy for DC-DC converters
T2 - Application to DC microgrid
AU - Rouzbehi, Kumars
AU - Miranian, Arash
AU - Escaño, Juan Manuel
AU - Rakhshani, Elyas
AU - Shariati, Negin
AU - Pouresmaeil, Edris
PY - 2019
Y1 - 2019
N2 - This paper develops a data-driven strategy for identification and voltage control for DC-DC power converters. The proposed strategy does not require a pre-defined standard model of the power converters and only relies on power converter measurement data, including sampled output voltage and the duty ratio to identify a valid dynamic model for them over their operating regime. To derive the power converter model from the measurements, a local model network (LMN) is used, which is able to describe converter dynamics through some locally active linear sub-models, individually responsible for representing a particular operating regime of the power converters. Later, a local linear controller is established considering the identified LMN to generate the control signal (i.e., duty ratio) for the power converters. Simulation results for a stand-alone boost converter as well as a bidirectional converter in a test DC microgrid demonstrate merit and satisfactory performance of the proposed data-driven identification and control strategy. Moreover, comparisons to a conventional proportional-integral (PI) controllers demonstrate the merits of the proposed approach.
AB - This paper develops a data-driven strategy for identification and voltage control for DC-DC power converters. The proposed strategy does not require a pre-defined standard model of the power converters and only relies on power converter measurement data, including sampled output voltage and the duty ratio to identify a valid dynamic model for them over their operating regime. To derive the power converter model from the measurements, a local model network (LMN) is used, which is able to describe converter dynamics through some locally active linear sub-models, individually responsible for representing a particular operating regime of the power converters. Later, a local linear controller is established considering the identified LMN to generate the control signal (i.e., duty ratio) for the power converters. Simulation results for a stand-alone boost converter as well as a bidirectional converter in a test DC microgrid demonstrate merit and satisfactory performance of the proposed data-driven identification and control strategy. Moreover, comparisons to a conventional proportional-integral (PI) controllers demonstrate the merits of the proposed approach.
KW - DC-dc power converter
KW - Hierarchical binary tree
KW - Takagi-sugeno fuzzy system
UR - http://www.scopus.com/inward/record.url?scp=85067081285&partnerID=8YFLogxK
U2 - 10.3390/electronics8050493
DO - 10.3390/electronics8050493
M3 - Article
AN - SCOPUS:85067081285
VL - 8
SP - 1
EP - 14
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 5
M1 - 493
ER -