A review of Volt/Var control techniques in passive and active power distribution networks
Bibliographic record
Abstract
Volt/Var control problem of distribution systems has been extensively investigated in literature. Many control models and algorithms have been proposed to achieve better system quality, security, reliability, efficiency, loadability, and cost effectiveness. Early distribution systems are built based on centralized power generation which is named passive distribution system (PDS), where power flow is unidirectional. Nowadays, the topology of the distribution system allows for bidirectional power flow which is named active distribution system (ADS) due to the presence of active resources, such as distributed generations (DGs). The complexity of controlling each system depends on the topology and size of the network, as well as the control devices used. However, in general there are mainly two main control strategies used to control power networks: centralized and decentralized. This paper provides a review for both control strategies in the distribution system based on Volt/Var control techniques. It introduces the most commonly used techniques and algorithms in the literature for passive and active distribution systems. Moreover, it provides the reader with a comprehensive review on the common optimization techniques and the different objective functions used in terms of loss minimization, voltage deviation, and minimum control variable operation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".