Probabilistic assessment of the impact of integrating large-scale high-power fast charging stations on the power quality in the electric power distribution systems
Bibliographic record
Abstract
The work presented in this thesis assesses the impacts of integrating large-scale high-power fast charging stations on the electric power quality by studying different power quality phenomena such as low-order and high-order harmonics, supraharmonics and voltage/light flickering. New three-phase effective power quantities are developed at both the low-order (harmonics below 40th order) and high order (harmonics above 40th order) and are used to quantify such harmonic impact. Chargers from two different manufacturers are used in this study and the real measurement are performed at fast charging stations in Canada. The Monte Carlo method is used to probabilistically estimate the electrical vehicles (EV) power demand when charging from the fast charging stations. The IEEE 34-bus standard test distribution system is employed to simulate the different impacts from different chargers??? manufactures. The results have shown that the chargers from different manufacturers may contribute differently in terms of the harmonic distortion levels reaching 18% at the system level. Furthermore, the frequency spectrum of the chargers from different manufacturers are different at both the low-order and high-order harmonics. The results have also shown that the new three-phase power quantities defined in this work are useful in identifying the chargers with high contribution to both the low-order and the high-order harmonics distortion/interference by separating the power quantities defined in the IEEE Standard 1459-2010 into several power quantities at the low-order harmonic (ranging from 2nd to 39th harmonic order or below 2.4 kHz) and the high-order harmonics (beyond 40th harmonic order or beyond 2.4 kHz).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".