Dynamic Modeling of Wound-Rotor Slip-Ring Induction Generator with Switched-Excitation Capacitance and Chopper Resistance Across Bridge Rectifier in the Rotor Circuit
Bibliographic record
Abstract
This paper presents a novel external secondary self-excitation capacitance and chopper resistance across the diode bridge rectifier in the rotor circuit to control the voltage and frequency of 3-phase, wound-rotor, slip-ring, induction generator (WRSRIG). The problem related to the excitation- capacitance from the rotor side is the required large excitation capacitance to enhance the generator performance in a wide - range. In the present proposed method, a dynamic excitation capacitance is used in the H-Bridge inverter circuit connected in series with a chopper resistance across a 3-phase diode bridge rectifier in the rotor circuit. The duty ratios of the H-bridge thyristor elements and the thyristor chopper element are varied to emulate the excitation-capacitance as well as external-rotor resistance values dynamically, and to be used as terminal voltage and its frequency control. The dynamic excitation-capacitance is used to control the terminal voltage, while the dynamic external rotor resistance is used to control the generator speed (frequency). To study the performance of the WRSRIG a dynamic model is presented in this paper in D-Q axes rotating synchronously in a reference frame. The dynamic model takes into account all the machine parameters, such as stator and rotor - iron core losses, stator and rotor - stray load losses, dynamic saturation of the magnetizing inductance, rotor harmonic losses generated due to switching action of the bridge rectifier, reduction voltage in the rotor circuit due to the over-lap phenomenon and rotor external resistance losses. The new proposed equivalent circuit of the WRSRIG will become an efficient method for studying the performance characteristics of the generator and to be used as a suitable tool for an algorithm of the vector control analysis.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".