AI &amp; Physics-Based Bad Command/Data Detection in Large Power Electronics Systems: Multi-Port Autonomous Reconfigurable Solar Power Plant (MARS)

Debnath, Suman; Marthi, Phani Ratna Vanamali; Xia, Qianxue; Lee, HyoJong; Pan, Jiuping; Nuqui, Reynaldo

doi:10.1109/tpwrd.2023.3281293

AI & Physics-Based Bad Command/Data Detection in Large Power Electronics Systems: Multi-Port Autonomous Reconfigurable Solar Power Plant (MARS)

Journal Article · Mon Jun 05 00:00:00 EDT 2023 · IEEE Transactions on Power Delivery

DOI:https://doi.org/10.1109/tpwrd.2023.3281293· OSTI ID:1986213

^[1]; ^[1]; Xia, Qianxue ^[2]; Lee, HyoJong ^[3]; Pan, Jiuping ^[3]; Nuqui, Reynaldo ^[3]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Georgia Institute of Technology, Atlanta, GA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Hitachi Energy, Raleigh, NC (United States)

Detection of bad data from measurement sensors and bad commands from control centers need to be carried out to avoid instabilities within large power electronics systems. Towards the same, in this paper, model and data driven methods are proposed to identify anomalies in measured data and commands received by large power electronics systems. The large power electronics system considered in this paper is a multi-port autonomous reconfigurable solar power plant (MARS), which consists of photovoltaic (PV) and energy storage systems (ESSs) that connect to high-voltage direct current (HVdc) system and transmission ac power grid. The proposed algorithms in the MARS power plant to detect bad data from measurements and bad commands from control centers are evaluated in simulations and hardware-in-the-loop (HIL) tests. Furthermore, it has been observed that the proposed algorithms are able to detect bad measurements and commands in all the use cases evaluated.

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:: 1986213

Journal Information:: IEEE Transactions on Power Delivery, Journal Name: IEEE Transactions on Power Delivery Journal Issue: TBD Vol. TBD; ISSN 0885-8977

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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