TY - JOUR
T1 - Adaptive TS Fuzzy-based MPC for DC Microgrids with Dynamic CPLs
T2 - Nonlinear Power Observer Approach
AU - Vafamand, Navid
AU - Yousefizadeh, Shirin
AU - Khooban, Mohammad Hassan
AU - Bendtsen, Jan Dimon
AU - Dragicevic, Tomislav
PY - 2019/9
Y1 - 2019/9
N2 - The performance of a DC microgrid (MG) might degrade because of the dynamics of constant power loads (CPLs). In this paper, a novel adaptive controller is proposed to mitigate the destructive effect of time-varying uncertain CPLs. A nonlinear disturbance observer is developed to estimate the instantaneous power of the CPLs. The estimated CPLs powers are then employed in a Takagi–Sugeno fuzzy-based model predictive control strategy, aiming to adaptively modify the injecting current of the energy storage system. The proposed approach is applied to a dc MG testbed that feeds one CPL. Experimental results show that the proposed adaptive controller is able to increase the stability margin and improve the transient response of the dc MG.
AB - The performance of a DC microgrid (MG) might degrade because of the dynamics of constant power loads (CPLs). In this paper, a novel adaptive controller is proposed to mitigate the destructive effect of time-varying uncertain CPLs. A nonlinear disturbance observer is developed to estimate the instantaneous power of the CPLs. The estimated CPLs powers are then employed in a Takagi–Sugeno fuzzy-based model predictive control strategy, aiming to adaptively modify the injecting current of the energy storage system. The proposed approach is applied to a dc MG testbed that feeds one CPL. Experimental results show that the proposed adaptive controller is able to increase the stability margin and improve the transient response of the dc MG.
KW - DC microgrid (MG)
KW - Takagi–Sugeno (TS) fuzzy model
KW - model predictive controller (MPC)
KW - non-ideal constant power load (CPL)
KW - nonlinear power observer
UR - http://www.scopus.com/inward/record.url?scp=85057891548&partnerID=8YFLogxK
U2 - 10.1109/JSYST.2018.2880135
DO - 10.1109/JSYST.2018.2880135
M3 - Journal article
SN - 1932-8184
VL - 13
SP - 3203
EP - 3210
JO - I E E E Systems Journal
JF - I E E E Systems Journal
IS - 3
M1 - 8552394
ER -