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Record W3007860572 · doi:10.1049/iet-gtd.2019.1841

Reactive power planning using convex line‐wise power balance equations for radial distribution systems

2020· article· en· W3007860572 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIET Generation Transmission & Distribution · 2020
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsToronto Metropolitan University
FundersMitacs
KeywordsPower BalancePower (physics)Balance (ability)Line (geometry)Regular polygonAC powerElectric power systemDistribution (mathematics)Control theory (sociology)MathematicsMathematical optimizationComputer scienceMathematical analysisPhysicsGeometry

Abstract

fetched live from OpenAlex

Optimal capacitor placement for radial distribution systems (RDSs) considers minimising the total cost of new fixed capacitors, switchable capacitors, and losses, while satisfying power balance equations, limits on bus voltages and capacitor limits. It is a non‐convex mixed‐integer non‐linear programming (MINLP) challenge. In this study, the authors propose a solution method using a line‐wise model (LWM) of power balance equations. First, equations for LWM are presented with their Jacobian for solving the power flow problem using Newton–Raphson method. Then, an optimal non‐convex MINLP capacitor placement formulation with LWM power balance equations is presented. Thereafter, it is transformed into a convex mixed‐integer conic programming formulation using second‐order conic relaxation. Both the non‐convex and convex optimal capacitor placement formulations are used to study 69‐bus and 136‐bus RDS. The results are compared with a formulation that uses the branch flow model (BFM) for power balance equations. Results show that the non‐convex LWM‐based formulation is twice as fast when compared with the BFM‐based formulation. The convex LWM‐based formulation is from 4 to 30 times as fast when compared with the BFM‐based formulation, demonstrating the benefits of the use of the LWM‐based formulation for enhancing the solution space of the optimisation problem.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.042
GPT teacher head0.267
Teacher spread0.225 · 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