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Record W2335956429 · doi:10.1109/tpwrs.2015.2456037

Delay-Dependent Stability Control for Power System With Multiple Time-Delays

2015· article· en· W2335956429 on OpenAlexfundno aff
Jian Li, Zhaohui Chen, Dongsheng Cai, Zhen Wei, Qi Huang

Bibliographic record

VenueIEEE Transactions on Power Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsnot available
FundersChina Scholarship CouncilUniversity of Science and Technology of ChinaUniversity of Electronic Science and Technology of ChinaChongqing University of Science and TechnologyNational Natural Science Foundation of ChinaUniversity of Alberta
KeywordsElectric power systemControl theory (sociology)Controller (irrigation)Stability (learning theory)Linear matrix inequalityComputer sciencePower (physics)Control systemControl (management)Control engineeringMathematicsEngineeringMathematical optimization

Abstract

fetched live from OpenAlex

Time-delay exists widely in electric power systems, and is found to have significant effect on the performance of operation and control under certain conditions. It is shown that even a very small delay may destabilize the power system. Therefore, time-delay is of important concern and should be properly handled, especially in the wide-area measurement and control environment. However, only few results about the controller design for power system considering multiple time-delays are reported. In this paper, a multiple time-delayed power system model is constructed with power system stabilizer (PSS) considering time-delays. By using Lyapunov stability theory and linear matrix inequality (LMI) method, two H∞control schemes are developed for time-varying multiple delayed systems. The proposed controllers guarantee the closed-loop system asymptotic stable with H∞performance. A two-area four-machine power system and the New England 10-machine 39-bus system are employed to demonstrate the effectiveness of proposed methods. The simulation results verify that the designed controllers can improve the control performance significantly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.201
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations143
Published2015
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Power SystemsSame topicPower System Optimization and StabilityFrench-language works237,207