Optimal Placement of Capacitors in Electrical Networks with Renewable Energy Sources to Improve Power Quality and Reduce Costs
Bibliographic record
Abstract
This paper presents a novel method to optimally place capacitors in power systems that incorporate renewable energy resources and sensitive non-linear loads, considering system uncertainties. A voltage severity index is introduced to assist system operators in the planning and programming of capacitors. The effectiveness of the proposed method is evaluated on a real large copper mine network in Iran, equipped with sensitive loads to voltage sags and doubly fed induction generators (DFIGs). The results indicate that the control mode of DFIGs and their output power influence the optimal location and size of capacitors. The proposed capacitor placement method enhances voltage profiles, reduces total harmonic distortion, minimizes grid losses, and lowers the costs associated with capacitors. Furthermore, the results underscore the necessity of selecting the appropriate control method based on the system’s operational priorities and conditions. The proposed capacitor placement method successfully reduced financial losses due to voltage sags up to 62%, leading to more secure operation of sensitive loads.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".