Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1735173

Virtual synchronous generator system and method with virtual inertia control

Gong, Maozhong
View original source ↗Read source document ↗Export metadata ↓

Abstract

A virtual synchronous generator device disclosed. The device includes an inverter and inverter controller having a power control portion, a power reserve portion, a power point tracking control portion, and a virtual inertia control portion. The power reserve portion determines an amount of power to be reserved and sends a signal, indicative of the determined amount of power to the power point tracking controller. The power point tracking controller determines a power point that is less than a MPP, and provides a signal to the power control portion indicative of the determined power point. The inertia control portion determines a virtual inertia and provides a signal indicative of the virtual inertia to the power control portion. The power control portion provides a power control signal to the inverter based on the power point tracking signal and the inertia command signal.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gong, Maozhong. 2020-08-18. Virtual synchronous generator system and method with virtual inertia control. https://www.osti.gov/biblio/1735173

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗