Authenticated voltage control of partitioned power networks with optimal allocation of STATCOM using heuristic algorithm
Bibliographic record
Abstract
This study presents a secondary voltage control based on an optimisation algorithm to locate the control buses and regulate the voltage. Control buses are the buses where compensators will be installed on these buses to regulate voltage and avoid voltage violations. Firstly, partitioning algorithm using fuzzy C‐means has been implemented on the power network. Partitioning techniques split the power system into regions to avoid the propagation of disturbances between regions by using local controllers. Then, a number of buses are labelled as control buses displaying the critical point for voltage control in each region. The control algorithm is a decentralised controller which tries to eliminate voltage violations in power system resulting from load variations and disturbances. The decentralised controllers are implemented using flexible AC transmission system (FACTS) devices such as static synchronous compensator (STATCOM). The methodology is applied to the IEEE 118‐bus network. The results show the performance and ability of the partitioning algorithm and bus control selection to regulate the voltage and avoid propagation of disturbances between regions.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".