Smart Grid Connection of an Induction Motor Using a Three-Phase Floating H-bridge System as a Series Compensator
Bibliographic record
Abstract
Electrical grid voltage sags are a significant industrial power quality concern. According to a survey result across the US, voltage sags and short-duration power outages are responsible for 92% of power quality problems faced by industrial customers. These power interruptions often impose severe cost penalties in plant shutdowns for many industries. A series compensation scheme for an induction motor is presented with inherent voltage sag ride through capability. The system utilizes a system of three-phase floating capacitor H-bridge converters located in each phase between the utility grid and a squirrel-cage induction motor. By injecting a series voltage in each phase, the proposed system can manipulate the voltage supplied to a motor, increasing its tolerance of grid voltage sags. The voltage injection scheme has an inherently leading grid power factor under steady state and, hence, generates VARs into the grid, over a wide range of load conditions. The paper develops mathematical analysis of the proposed system to quantify both the voltage sag ride-through and the reactive power generation that results. The analysis shows that voltage sag tolerance of the proposed system is closely related to the motor fundamental power factor. Moreover, it is found that unity power factor operation of the system, as seen from the grid, is possible and that the reactive power generation capability can also be accurately quantified. A 5-hp experimental testbed is used to validate both the grid voltage ride-through capability feature and the reactive power generation characteristics.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".