A Neural Network-Based Power Mismatch Elimination Strategy for Integrated Solar and ESS AC/DC Systems (MARS)

Xia, Qianxue; Debnath, Suman; Saeedifard, Maryam

doi:10.1109/TIE.2024.3508054

A Neural Network-Based Power Mismatch Elimination Strategy for Integrated Solar and ESS AC/DC Systems (MARS)

Journal Article · Thu Dec 19 00:00:00 EST 2024 · IEEE Translations on Industrial Electronics

DOI:https://doi.org/10.1109/TIE.2024.3508054· OSTI ID:2573682

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Georgia Inst. of Technology, Atlanta, GA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Georgia Institute of Technology, Atlanta, GA (United States)

The multiport autonomous reconfigurable solar power plant (MARS) is a promising concept for the integration of photovoltaic (PV) and energy storage system (ESS) to the transmission ac grid and a high-voltage direct current (HVdc) link. The presence of PV and ESS in each arm of the MARS results in uneven distribution of active power among different submodules (SMs), thereby leading to unbalanced SM capacitor voltages and potentially compromising the system stability. Moreover, in the case of partial shadings, shaded PV SMs will suffer from decreased power injections causing power mismatch in the MARS system. To address this issue, a neural-network-based power mismatch elimination (NNPME) strategy is proposed in this article. The proposed NNPME strategy optimizes ESS usage and leverages both dc and ac circulating currents to facilitate power transfer among the SMs, arms, and phases of the MARS system. Simulation and control hardware-in-the-loop (cHIL) experiments demonstrate the effectiveness of the proposed NNPME strategy. Compared with the traditional approaches, the proposed NNPME strategy can significantly enhance system efficiency and ensure stable and continuous operation, even in the presence of uneven power distribution within the MARS system.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: Office of Energy Efficiency and Renewable Energy

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2573682

Journal Information:: IEEE Translations on Industrial Electronics, Journal Name: IEEE Translations on Industrial Electronics Journal Issue: 7 Vol. 72; ISSN 1557-9948; ISSN 0278-0046

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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A Neural Network-Based Power Mismatch Elimination Strategy for Integrated Solar and ESS AC/DC Systems (MARS)

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