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Record W4210654753 · doi:10.1002/9781119736592.ch4

Grid Connection Studies

2022· other· en· W4210654753 on OpenAlexaff
Davood Naghaviha, Hassan Nikkhajoei, Houshang Karimi

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPhotovoltaic systemGrid-connected photovoltaic power systemGridSimulation softwareSoftwarePower stationEngineeringGrid connectionPower (physics)Automotive engineeringElectrical engineeringMaximum power point trackingComputer scienceOperating systemVoltage

Abstract

fetched live from OpenAlex

Connecting distributed generation sources such as photovoltaic (PV) power plants to the power grid affects its operation, stability, and safety. Technical studies of the grid connection of a PV power plant are performed using an advanced simulation software based on the national network codes and standards. This chapter explains the importance of conducting studies before connecting a PV power plant to the grid. PV power plant grid connection studies are performed with power system simulation software, including DIgSILENT, Etap, Cyme, PSS-E, EMTP, and PSCAD. The chapter describes the information required for the modeling of a PV power plant and the power network. A sample PV power plant connected to the grid is modeled in DIgSILENT software. After receiving the power network and PV plant information, the grid-connected PV plant is modeled using a network analysis software such as DIgSILENT.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.031
Threshold uncertainty score0.970

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)0.0310.000

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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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