Machine Learning-Assisted Stability Boundary Determination of Multiport Autonomous Reconfigurable Solar Power Plants

Xia, Qianxue; Debnath, Suman; Saeedifard, Maryam

doi:10.1109/tie.2024.3383011

Machine Learning-Assisted Stability Boundary Determination of Multiport Autonomous Reconfigurable Solar Power Plants

Journal Article · Thu Apr 18 00:00:00 EDT 2024 · IEEE Translations on Industrial Electronics

DOI:https://doi.org/10.1109/tie.2024.3383011· OSTI ID:2439003

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Georgia Institute 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 (MARS) power plant is a promising solution to integrate renewable resources and energy storage systems into the alternating current (ac) power grid and an high-voltage direct current (HVdc) link. In the MARS system, various input power sources are connected to the individual submodules (SMs) through direct current (dc)–dc converters. However, the presence of external power sources can result in unbalanced capacitor voltages of SMs, thereby violating stability constraints under multiple/diverse operating conditions. This article aims to address the gap by accurately determining the stability boundary of the MARS system. As such, a novel machine learning (ML)-assisted energy balancing control (EBC) criterion is proposed. Further, in conjunction with a refined EBC, this approach ensures balanced capacitor voltages across various types of SMs, significantly enhancing the overall system efficiency. The proposed EBC criterion effectively controls EBC activation and deactivation, achieving remarkable accuracy. Both power systems computer aided design (PSCAD)/electromagnetic transients including direct current (EMTDC) simulations and control hardware-in-the-loop (cHIL) tests are conducted to validate the feasibility and efficiency of the proposed method. By combining the EBC and ML-assisted EBC criterion, efficient energy management is achieved for systems featuring multiple input power sources, such as MARS. This approach enables the system to fully exploit its potential across an expanded operational range while upholding high-efficiency standards.

View Accepted Manuscript (DOE)

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

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2439003

Journal Information:: IEEE Translations on Industrial Electronics, Journal Name: IEEE Translations on Industrial Electronics Journal Issue: 11 Vol. 71; ISSN 0278-0046

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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