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Record W2546462601 · doi:10.1109/ccece.2016.7726671

Implementing self-healing distribution systems via fault location, isolation and service restoration

2016· article· en· W2546462601 on OpenAlexaff
Richard Guo, Chris Qu, Vidya Vankayala, Eugene Crozier, Stephen Allen, Kunle Adeleye

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsBC Hydro (Canada)Powertech Labs (Canada)
Fundersnot available
KeywordsInteroperabilityFault managementDistribution management systemFault (geology)AutomationSmart gridComputer scienceSystems engineeringService (business)Electric power systemReliability engineeringFault detection and isolationIsolation (microbiology)Self-healingDistributed computingEngineeringPower (physics)Electrical engineeringOperating systemArtificial intelligence

Abstract

fetched live from OpenAlex

BC Hydro and Powertech have developed a real-world outdoor integration and interoperability test yard for distributed automation technologies at Powertech Labs referred to as the Smart Utility Test Center (SUTC). The SUTC contains live 25kV power distribution equipment, telecommunications, data collection and management systems interconnected with a commercial Distribution Management System (DMS). FLISR (Fault Location, Isolation and Service Restoration) is a Distribution Management System feature to support implementation of self-healing power distribution systems. This presentation will provide an overview of the FLISR as well as the practical aspects of modeling power system networks to support FLISR functions. This paper also discusses how the DMS power system models and FLISR applications are optimized in the SUTC environment to improve the accuracy and performance of self-healing configurations. A sample case study of improvement in SAFI and SAIDI is presented based on an outage simulation with and without self-healing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.741
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.211
Teacher spread0.204 · 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 teacher head, 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

Citations3
Published2016
Admission routes1
Has abstractyes

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