Combined approach for optimal placement and sizing capacitors in RDN
Bibliographic record
Abstract
Purpose Reactive power in radial distribution networks (RDN) leads to detrimental effects like power factor degradation, voltage profile alterations and increased power losses, ultimately impacting network stability. This paper aims to present a novel two-phase optimization approach to address the challenging task of locating, sizing and determining the optimal number of capacitors in RDNs. Design/methodology/approach The first step of the proposed methodology is using a hybrid technique that combines the loss sensitivity factors (LSF) with voltage sensitivity factors (VSF) to identify network nodes requiring capacitor installation efficiently. The second step uses an external approximation technique to optimize the size and number of capacitors for each identified node, achieving significant power loss reductions. Findings The effectiveness of this new approach is evaluated on two RDNs: 33- and 69-bus. Simulations on these test systems demonstrate the effectiveness of the proposed approach, reducing total power loss by 34.7% in the first case and 35.3% in the second. The method’s robustness compared to other approaches further highlights its potential for practical implementation in RDNs, contributing to improved network stability and efficient power distribution. Originality/value This paper presents a novel, efficient and robust approach to determining the optimal number, location and size of an RDN capacitor. The problem is addressed through a new formulation with modified constraints. The method consists of two stages: initially, a hybrid LSF–VSF method identifies potential capacitor locations, followed by an external approximation-based mixed-integer nonlinear programming (MINLP) solver to optimize capacitor numbers and sizes. The proposed methodology is applied to the widely used 33-bus and 69-bus RDN test systems. Comparative analysis with existing methods highlights the proposed approach’s effectiveness. Key contributions of this study include the following: Proposes a new problem formulation with modified constraints. Proposes a novel two-stage framework for optimally locating and sizing capacitors in RDNs. Introduces a hybrid LSF–VSF algorithm to identify promising capacitor locations efficiently. Using an external approximation-based MINLP for optimal sizing. Demonstrates the effectiveness of the proposed approach through rigorous testing on standard benchmark systems. Provides a comprehensive comparative analysis against state-of-the-art methods, highlighting the proposed approach’s superior performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".