Processing Magnetometer Signals for Accurate Wide-Area Geomagnetic Disturbance Monitoring and Resilience Analysis
Bibliographic record
Abstract
Geomagnetic disturbances (GMDs) can disrupt the operation of power systems by inducing a quasi-dc voltage and generating geomagnetically induced currents (GICs) in a vast area of the power systems. This gives rise to the importance of wide-area monitoring of magnetic field on earth's surface. Assessment of power system resiliency against GMDs requires an accurate calculation of GIC flows, which is achieved by wide-area monitoring of the magnetic field B, and processing the B signals that are recorded by magnetometers on the earth's surface. In this paper, a method is proposed to denoise the B signal. Spikes in the signal are detected using a stationary wavelet transform and then replaced. Time derivative of B signal is taken by a continuous wavelet transform to prevent amplification of the noises. Furthermore, a quantitative analysis is performed to detect the optimum sampling frequency to overcome the practical limitations associated with transmitting the recorded B signal and to modify peaks of dB/dt signal negligibly. It is demonstrated that a sampling frequency of 1/15 Hz satisfies these conditions. Finally, GICs in a 118-bus benchmark power system are calculated with respect to a realistic geomagnetic storm to demonstrate the effectiveness of the proposed signal processing 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 | 0.001 |
| 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".