Selecting Locations and Sizes of Battery Storage Systems Based on the Frequency of the Center of Inertia and Principle Component Analysis
Bibliographic record
Abstract
This article develops, implements, and tests a method for selecting the locations and sizes of battery storage systems (BSSs) for power systems with distributed power generation. The developed method is based on calculating the coherency index for each bus to determine its contribution to the frequency of the center of inertia (COI) during and post a transient event. Once the coherency index is calculated for all buses, the principle component analysis (PCA) is used to identify the buses with consistent low values of the coherency index. These buses are considered as candidate locations for BSSs. In addition, the lowest values of the coherency index are used to determine the required sizes of BSSs to be connected at the buses with low contributions to the frequency of the COI. The performance of the PCA with COI (PCA-COI) method is evaluated for the Barbados power system for various transient events. Performance results show that BSSs (selected using the proposed method) can effectively improve the frequency stability with minor sensitivity to the levels of distributed power generation, loading levels, and/or type or location of transient events.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".