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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".