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DOE OSTI2021

Fault location in High Voltage Multi-terminal dc Networks Using Ensemble Learning

Precise location of faults for large distance power transmission networks is essential for faster repair and restoration process. High Voltage direct current (HVdc) networks using modular multi-level converter (MMC) technology has found its prominence for interconnected multi-terminal networks. This allows for large distance bulk power transmission at lower costs. However, they cope with the challenge of dc faults. Fast and efficient methods to isolate the network under dc faults have been widely studied and investigated. After successful isolation, it is essential to precisely locate the fault. The post-fault voltage and current signatures are a function of multiple factors and thus accurately locating faults on a multi-terminal network is challenging. In this paper, we discuss a novel data-driven ensemble learning based approach for accurate fault location. Here we utilize the eXtreme Gradient Boosting (XGB) method for accurate fault location. The sensitivity of the proposed algorithm to measurement noise, fault location, resistance and current limiting inductance are performed on a radial three-terminal MTdc network designed in Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Flexible Large Power Solid State Transformer (FLP-SST)

A large power transformer (LPT) is a major component in the electric power system grid. With the passage of time their reliability in terms of failure and service maintenance has been a big concern. According to a report by EPRI, various agencies have emphasized, and recent events have demonstrated, the critical nature of power transformers in the face of possible high-impact, low-frequency (HILF) events. The HILF events include intentional malicious events (e.g., physical attacks, cyber-attacks, coordinated attacks, electromagnetic pulse weapons, and others), natural disasters (e.g., hurricanes, earthquakes, severe geomagnetic disturbances, etc.), and non-intentional or accidental events such as nuclear power plant accidents. With aging, the line frequency transformers (LFTs) are also prone to failure due to insulation degradation, coolant leakage, mechanical failure, inadequate maintenance, etc. and thus poses a bigger threat to the electrical grid. Moreover, these transformers are custom made to fit the needs at the substation and weighs approximately between 100-400 tons, hence are neither interchangeable nor stored as a spare one. Hence reasonable efforts need to be made regarding the replacement of the LPT with a more feasible technology that does not pose similar issues. The concept of Flexible Large Power Solid-State Transformer (FLPSST) has addressed the aforementioned issues to provide an economical solutions for the failure of LPTs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Capacitor Voltage Ripple Estimation and Optimal Sizing of Modular Multi-Level Converters for Variable-Speed Drives

This article deals with the sizing of submodule capacitors for modular multilevel converters (MMCs) used for variable speed drives for medium voltage and high voltage electric machines. Variable speed drives are required to run over a wide frequency range from zero to a few hundred Hz or even over 1 kHz. Under low frequency operation (<30 Hz), a large current ripple flowing through the MMC submodule capacitors will cause significant capacitor voltage fluctuation. This article proposes an optimal submodule capacitor selection method based on the common-mode voltage injection. The proposed sizing model includes two functions: 1) accurately estimating the capacitor voltage ripple based on the drive system parameters; and 2) identifying the minimum capacitance of the submodule capacitors based on the allowed maximum capacitor voltage fluctuations. Simulation and experimental results show that the error between the actual and estimated values of the voltage fluctuation is within 3% using the proposed model.

42 ENGINEERING↗