Assessing and Mitigating Impacts of Electric Vehicle Harmonic Currents on Distribution Systems
Bibliographic record
Abstract
Harmonic currents of electric vehicle (EV) chargers could jeopardize the power quality of distribution systems and add to the transformer’s losses, thus degrading its lifetime. This paper assesses and mitigates the impacts of different EV chargers on distribution transformers and the voltage quality of distribution systems. The effect of state-of-charge (SOC) of the EV battery is considered through applying weighted arithmetic mean to accurately assess the impacts of EV harmonic currents on aging and losses of the EV interfacing transformer. The voltage quality of the IEEE 33-bus distribution system, supplying several EV parking lots, is also assessed at different charging levels using a fast-decoupled harmonic power flow. A new optimal harmonic power flow algorithm—that incorporates photovoltaic-based distribution generation units (DGs)—is developed to enhance the voltage quality of distribution systems, and elongate the lifetime of the substation transformer. The effectiveness of the proposed mitigation method is confirmed using the IEEE 33-bus distribution system, hosting several EV charging stations and photovoltaic-based DGs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".