TY - JOUR
T1 - Enhancing power quality in microgrids with a new online control strategy for DSTATCOM using reinforcement learning algorithm
AU - Bagheri, Mehdi
AU - Nurmanova, Venera
AU - Abedinia, Oveis
AU - Salay Naderi, Mohammad
N1 - Funding Information:
This work was supported by the Faculty Development Competitive Research Grant, Nazarbayev University, under Grant 090118FD5318.
Publisher Copyright:
© 2013 IEEE.
PY - 2018/7/3
Y1 - 2018/7/3
N2 - To mitigate the power quality issue in microgrids, a new online reference control strategy for distribution static compensator using the reinforcement learning algorithm is presented. The new controller is supposed to compensate the reactive power, harmonics, and unbalanced load current in a microgrid utilizing voltage and current parameters. Voltage controller is used to adjust the set point of the reactive power reference, whereas the current based controller tries to compensate the unbalanced load current in distributed resource network through the quadrature axis (q-axis) and zero axis (0-axis). The proposed control strategy is applied to an autonomous microgrid with a weak ac-supply (non-stiff source) distribution system under different loads as well as three-phase fault conditions. Different scenarios are studied and simulation results for various conditions are discussed. The performance of the proposed online secondary control strategy is also discussed in detail.
AB - To mitigate the power quality issue in microgrids, a new online reference control strategy for distribution static compensator using the reinforcement learning algorithm is presented. The new controller is supposed to compensate the reactive power, harmonics, and unbalanced load current in a microgrid utilizing voltage and current parameters. Voltage controller is used to adjust the set point of the reactive power reference, whereas the current based controller tries to compensate the unbalanced load current in distributed resource network through the quadrature axis (q-axis) and zero axis (0-axis). The proposed control strategy is applied to an autonomous microgrid with a weak ac-supply (non-stiff source) distribution system under different loads as well as three-phase fault conditions. Different scenarios are studied and simulation results for various conditions are discussed. The performance of the proposed online secondary control strategy is also discussed in detail.
KW - DSTATCOM control
KW - microgrid management
KW - online control
KW - power quality enhancement
KW - reactive power control
KW - reinforcement learning
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U2 - 10.1109/ACCESS.2018.2852941
DO - 10.1109/ACCESS.2018.2852941
M3 - Article
AN - SCOPUS:85049446229
SN - 2169-3536
VL - 6
SP - 38986
EP - 38996
JO - IEEE Access
JF - IEEE Access
M1 - 8403205
ER -