Structural modularity is critical to solid-state transformer (SST) and solid-state power substation (SSPS) concepts, but operational aspects related to this modularity are not yet fully understood. Previous studies and demonstrations of modular power conversion systems assume identical module compositions, but dependence on module uniformity undercuts the value of the modular framework. In this project, a hierarchical control approach was developed for modular SSTs which achieves system-level objectives while ensuring equitable power sharing between nonuniform building block modules. This enables module replacements and upgrades which leverage circuit and device technology advancements to improve system-level performance. The functionality of the control approach is demonstrated in detailed time-domain simulations. Results of this project provide context and strategic direction for future LDRD projects focusing on technologies supporting the SST crosscut outcome of the resilient energy systems mission campaign.
This report is a summary of a 3-year LDRD project that developed novel methods to detect faults in the electric power grid dramatically faster than today’s protection systems. Accurately detecting and quickly removing electrical faults is imperative for power system resilience and national security to minimize impacts to defense critical infrastructure. The new protection schemes will improve grid stability during disturbances and allow additional integration of renewable energy technologies with low inertia and low fault currents. Signal-based fast tripping schemes were developed that use the physics of the grid and do not rely on communication to reduce cyber risks for safely removing faults.
This quick note outlines what we found after our conversion with you and your team. As suggested, we loaded 1547-2003 source requirements document (SRD) and then went back and loaded 1547-2018 SRD. This did result in implementing the new 1547-2018 settings. This short report focuses on the frequency-watt function and shows a couple of screen shots of the parameter settings via the Mojave HMI interface and plots of the results of the inverter with FW function enabled in both default and most aggressive settings response to frequency events. The first screen shot shows the 1547-2018 selected after selecting 1547-2003.
Understanding of semiconductor breakdown under high electric fields is an important aspect of materials’ properties, particularly for the design of power devices. For decades, a power-law has been used to describe the dependence of material-specific critical electrical field (Ecrit) at which the material breaks down and bandgap (Eg). The relationship is often used to gauge tradeoffs of emerging materials whose properties haven’t yet been determined. Unfortunately, the reported dependencies of Ecrit on Eg cover a surprisingly wide range in the literature. Moreover, Ecrit is a function of material doping. Further, discrepancies arise in Ecrit values owing to differences between punch-through and non-punch-through device structures. We report a new normalization procedure that enables comparison of critical electric field values across materials, doping, and different device types. An extensive examination of numerous references reveals that the dependence Ecrit ∝ Eg1.83 best fits the most reliable and newest data for both direct and indirect semiconductors. Graphical abstract: [Figure not available: see fulltext.].
Deep level defects in wide bandgap semiconductors, whose response times are in the range of power converter switching times, can have a significant effect on converter efficiency. Here, we use deep level transient spectroscopy (DLTS) to evaluate such defect levels in the n-drift layer of vertical gallium nitride (v-GaN) power diodes with VBD ~ 1500 V. DLTS reveals three energy levels that are at ~0.6 eV (highest density), ~0.27 eV (lowest density), and ~45 meV (a dopant level) from the conduction band. Dopant extraction from capacitance–voltage measurement tests (C–V) at multiple temperatures enables trap density evaluation, and the ~0.6 eV trap has a density of 1.2 × 1015 cm-3. The 0.6 eV energy level and its density are similar to a defect that is known to cause current collapse in GaN based surface conducting devices (like high electron mobility transistors). Analysis of reverse bias currents over temperature in the v-GaN diodes indicates a predominant role of the same defect in determining reverse leakage current at high temperatures, reducing switching efficiency.
Interest in the application of DC Microgrids to distribution systems have been spurred by the continued rise of renewable energy resources and the dependence on DC loads. However, in comparison to AC systems, the lack of natural zero crossing in DC Microgrids makes the interruption of fault currents with fuses and circuit breakers more difficult. DC faults can cause severe damage to voltage-source converters within few milliseconds, hence, the need to quickly detect and isolate the fault. In this paper, the potential for five different Machine Learning (ML) classifiers to identify fault type and fault resistance in a DC Microgrid is explored. The ML algorithms are trained using simulated fault data recorded from a 750 VDC Microgrid modeled in PSCAD/EMTDC. The performance of the trained algorithms are tested using real fault data gathered from an operational DC Microgrid located on the Kirtland Air Force Base. Of the five ML algorithms, three could detect the fault and determine the fault type with at least 99% accuracy, and only one could estimate the fault resistance with at least 99% accuracy. By performing a self-learning monitoring and decision making analysis, protection relays equipped with ML algorithms can quickly detect and isolate faults to improve the protection operations on DC Microgrids.
DC microgrids envisioned with high bandwidth communications may well expand their application range by considering autonomous strategies as resiliency contingencies. In most cases, these strategies are based on the droop control method, seeking low voltage regulation and proportional load sharing. Control challenges arise when coordinating the output of multiple DC microgrids composed of several Distributed Energy Resources. This paper proposes an autonomous control strategy for transactional converters when multiple DC microgrids are connected through a common bus. The control seeks to match the external bus voltage with the internal bus voltage balancing power. Three case scenarios are considered: standalone operation of each DC microgrid, excess generation, and generation deficit in one DC microgrid. Results using Sandia National Laboratories Secure Scalable Microgrid Simulink library, and models developed in MATLAB are compared.
The Port of Alaska in Anchorage enables the economic vitality of the Municipality of Anchorage and State of Alaska. It also provides significant support to defense activities across Alaska, especially to the Joint Base Elmendorf-Richardson (JBER) that is immediately adjacent to the Port. For this reason, stakeholders are interested in the resilience of the Ports operations. This report documents a preliminary feasibility analysis for developing an energy system that increases electric supply resilience for the Port and for a specific location inside JBER. The project concept emerged from prior work led by the Municipality of Anchorage and consultation with Port stakeholders. The project consists of a microgrid with PV, storage and diesel generation, capable of supplying electricity to loads at the Port a specific JBER location during utility outages, while also delivering economic value during blue-sky conditions. The study aims to estimate the size, configuration and concept of operations based on existing infrastructure and limited demand data. It also explores potential project benefits and challenges. The report goal is to inform further stakeholder consultation and next steps.