Voltage Collapse Owing to Cascading Failures Under Geomagnetic Disturbances in Electromagnetic Transient Perspective
Bibliographic record
Abstract
Under severe geomagnetic disturbances (GMDs) caused by intense solar activity, transformers in the power grid will generate groups of massive reactive power losses and harmonic currents. At the same time, harmonic currents can readily cause the mal-operation of relay protection device for reactive power compensation, which puts the power grid in a state of insufficient reactive power, leading to local reactive power imbalances. This may cause cascading failures and even voltage collapse, resulting in a large-scale power blackout. This article analyzes the mechanism and triggering conditions of cascading failures, proposes the development process of voltage collapse caused by sensitive equipment in the power grid. Based on this, the electromagnetic transient simulation of a four-station eight-node and IEEE 118-GMD grid was used to simulate the voltage collapse owing to cascading failures under GMDs, verifying the correctness of the proposed theory. Further, the occurrence patterns of cascading failures in the power grid during geomagnetic storms were summarized based on the simulation result, and the triggering conditions and influencing factors leading to voltage collapse were identified. This provides guidance for accurately and quickly identifying risk nodes during geomagnetic storms invasion to permit timely disaster prevention measures to avoid voltage collapse under GMDs.
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 | 0.001 |
| 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 | 0.000 | 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".