TY - GEN
T1 - Maximum Power Point Tracking of Photovoltaic Systems Using Deep Q-networks
AU - Wang, Kangshi
AU - Hong, Dou
AU - Ma, Jieming
AU - Man, Ka Lok
AU - Huang, Kaizhu
AU - Huang, Xiaowei
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7/20
Y1 - 2020/7/20
N2 - A photovoltaic (PV) generator exhibits nonlinear current-voltage characteristics and its maximum power point varies with incident atmospheric conditions. Therefore, maximum power point tracking (MPPT) control is required to maximize the output power of the PV generator. In this paper, deep Q-network based reinforcement learning strategy is proposed to optimize MPPT process for the photovoltaic system. The proposed system uses a novel control method which introduces agent to interface with the environment and finally gets the strategy of maximum reward accordingly. Simulations and experiments show the feasibility and effectiveness of the proposed system. Compared with the traditional perturb and observe (PO) and incremental conductance (InC) methods, this method prominently saves tracking steps.
AB - A photovoltaic (PV) generator exhibits nonlinear current-voltage characteristics and its maximum power point varies with incident atmospheric conditions. Therefore, maximum power point tracking (MPPT) control is required to maximize the output power of the PV generator. In this paper, deep Q-network based reinforcement learning strategy is proposed to optimize MPPT process for the photovoltaic system. The proposed system uses a novel control method which introduces agent to interface with the environment and finally gets the strategy of maximum reward accordingly. Simulations and experiments show the feasibility and effectiveness of the proposed system. Compared with the traditional perturb and observe (PO) and incremental conductance (InC) methods, this method prominently saves tracking steps.
KW - deep Q-network
KW - maximum power point tracking
KW - photovoltaic systems
KW - reinforcement learning
UR - https://www.scopus.com/pages/publications/85111081682
U2 - 10.1109/INDIN45582.2020.9442100
DO - 10.1109/INDIN45582.2020.9442100
M3 - Conference Proceeding
AN - SCOPUS:85111081682
T3 - IEEE International Conference on Industrial Informatics (INDIN)
SP - 100
EP - 103
BT - Proceedings - 2020 IEEE 18th International Conference on Industrial Informatics, INDIN 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Industrial Informatics, INDIN 2020
Y2 - 21 July 2020 through 23 July 2020
ER -