TY - GEN
T1 - Deep Q-Network Based Global Maximum Power Point Tracking for Partially Shaded PV System
AU - Chou, Kuan Yu
AU - Yang, Chia Shiou
AU - Chen, Yon Ping
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/28
Y1 - 2020/9/28
N2 - Under the sun insolation in the daytime, the partially shaded effect would easily happen in a photovoltaic (PV) array due to clouds, trees, buildings, etc. To deal with the partially shaded effect, this paper proposes solar global maximum power point tracking (GMPPT) method based on Deep Q-Network. Maximum power point tracking (MPPT) is often used to achieve the maximum power in the PV system. The Perturbation and Observation (PO) method is one of the most popular MPPT techniques in practice. However, due to the use of fixed step size, the PO method may cause undesired oscillation around the maximum power point (MPP). With the partially shaded effect, the characteristic P-V curve of a PV array may possess multi-peaks, which often results in tracking of a local maximum, not the expected global maximum. Demonstrated by experiment results, the proposed Deep Q-Network based solar GMPPT method indeed can track the global MPP faster and more precisely without oscillation.
AB - Under the sun insolation in the daytime, the partially shaded effect would easily happen in a photovoltaic (PV) array due to clouds, trees, buildings, etc. To deal with the partially shaded effect, this paper proposes solar global maximum power point tracking (GMPPT) method based on Deep Q-Network. Maximum power point tracking (MPPT) is often used to achieve the maximum power in the PV system. The Perturbation and Observation (PO) method is one of the most popular MPPT techniques in practice. However, due to the use of fixed step size, the PO method may cause undesired oscillation around the maximum power point (MPP). With the partially shaded effect, the characteristic P-V curve of a PV array may possess multi-peaks, which often results in tracking of a local maximum, not the expected global maximum. Demonstrated by experiment results, the proposed Deep Q-Network based solar GMPPT method indeed can track the global MPP faster and more precisely without oscillation.
KW - Global maximum power point tracking (GMPPT)
KW - deep Q-network
KW - partially shaded photovoltaic (PV) system
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85098466075&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Taiwan49838.2020.9258116
DO - 10.1109/ICCE-Taiwan49838.2020.9258116
M3 - Conference contribution
AN - SCOPUS:85098466075
T3 - 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
BT - 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
Y2 - 28 September 2020 through 30 September 2020
ER -
