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Record W4362586090 · doi:10.24425/aee.2021.136055

Design and application of comprehensive evaluation index system of smart grid based on coordinated planning of major network and power distribution network

2021· article· en· W4362586090 on OpenAlexaff
Guangtao Ning, Bing Fang, Dan Qin, Yafeng Liang

Bibliographic record

VenueArchives of Electrical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicSmart Grid and Power Systems
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsPower networkIndex (typography)Power gridNetwork planning and designSmart gridComputer sciencePower (physics)Distribution (mathematics)GridElectric power systemReliability engineeringEngineeringElectrical engineeringTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

The comprehensive evaluation of the smart grid is of great significance to the development of the power grid. This study mainly analyzed the coordinated planning of major networks and power distribution networks of the grid. Firstly, the coordinated planning of major networks and power distribution networks was introduced, then a comprehensive evaluation index system was established based on six domains, i.e., economy, safety, reliability, coordination, environmental protection, and automation. The evaluation of the indexes was realized through the expert scoring method. Finally, taking the power grid planning of Boao Town, Qionghai City, Hainan Province, China, as an example, the current scheme and planning scheme were evaluated. The results showed that the planning scheme had better performance in aspects such as economy and reliability, and its score was 15.39% higher than the current scheme, which verifies the effectiveness of the planning scheme and its feasible application in practical projects.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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
GenreMethods

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

Citations5
Published2021
Admission routes1
Has abstractyes

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