Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1983986

Proactive Frequency Stability Scheme: A Distributed Framework Based on Particle Filters and Synchrophasors

Paramo, Gian·Bretas, Arturo (ORCID:0000000340060764)
View original source ↗Export metadata ↓

Abstract

The reactive nature of traditional under-frequency load shedding schemes can lead to delayed response and unnecessary loss of load. This work presents a proactive framework for power system frequency stability. Bayesian filters and synchrophasors are leveraged to produce predictions after disturbances are detected. By being able to estimate the future state of frequency corrective actions can be taken before the system reaches a critical condition. This proactive approach makes it possible to optimize the response to a disturbance, which results in a decrease in the amount of compensation utilized. The framework is tested via Matlab simulations based on Kundur’s Two-Area System, and the IEEE 14-Bus System. Performance metrics are provided and evaluated against other contemporary solutions found in literature. During testing this framework outperformed other solutions by drastically reducing the amount of load dropped during compensation.

Explore related subjects

42 ENGINEERINGfrequency stabilityparticle filterphasor measurement unitspower systems

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Paramo, Gian, Bretas, Arturo (ORCID:0000000340060764). 2023-06-05. Proactive Frequency Stability Scheme: A Distributed Framework Based on Particle Filters and Synchrophasors. https://doi.org/10.3390/en16114530

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↗