Identification and damping of low-frequency oscillations based on WAMS data and the revisited residue method – part I
Bibliographic record
Abstract
The results of low-frequency oscillations identification in the Republic of Kazakhstan power grid by using a Wide Area Measurement System are presented and an algorithm for damping low-frequency oscillations is proposed in this paper. Analysis of weakly damped inter-area low-frequency oscillations revealed a constant mode with a frequency range of 0.3‒0.4 Hz. It was determined that at these low-frequency oscillations, the amplitude of active power fluctuations along the transmission line was 150 MW with a duration of 9 minutes. The modal analysis calculation of the Republic of Kazakhstan power system model in the «DigSilent Power Factory» software shows the dangerous low-frequency oscillation modes having a damping ratio is 2.2 % and an eigenfrequency 0.328 Hz. These oscillation modes identified by the real data and in the developed model indicate the incorrect tuning of power system stabilizer parameters at power plants. It is necessary to retune the power system stabilizer parameters whenever changing the system’s and mode’s configurations. An analysis of existing power system stabilizer tuning methods was performed, and revisited residue method was determined as sufficiently effective. Thus, the developed algorithm for identification and damping of low-frequency oscillation consists of three tasks. The first task is data collection from the Wide Area Measurement System and Supervisory Control and Data Acquisition system and updating the calculation model based on the current status of equipment (generators, transformers, transmission lines, etc.). The second task is the identification of dangerous electromechanical oscillations and modal analysis based on information obtained in real-time. The third task is tuning the power system stabilizer parameters for damping dangerous low-frequency oscillation modes based on the revisited residue method
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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 | 0.001 |
| 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) | 0.001 | 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".