Geomagnetic Storm Warning via Phase-to-Phase Harmonic Distortion Analysis
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Solution Overview
Problem
Current forecasting techniques for solar eruptions are limited in predicting geomagnetically-induced currents (GICs) on power grids, often resulting in false warnings, delayed alerts, and unnecessary disruptions due to the lack of accurate correlation with power grid activity and the imprecision of magnetic indices.
Innovation Solution
A method that utilizes substorm activity data logs, harmonic distortion data streams, and geomagnetic variation measurements to predict future adverse events on power grids by computing phase-to-phase similarity and applying mathematical models to generate warnings based on geomagnetically-induced current-related solar activity, incorporating auroral magnetic activity indices to categorize severity and minimize disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If magnetic activity indices are used to forecast solar eruptions, then warnings can be generated, but the precision of prediction is insufficient leading to false warnings
Solution Approach 1:
The patent transforms magnetic activity indices from raw measurement values into symbolic representations that capture essential patterns. By changing the parameter representation from continuous magnetic index values to discrete symbolic states, the system achieves more reliable predictions while reducing the impact of measurement imprecision in the original magnetic indices.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes mathematical models and symbolic transformations between the raw magnetic activity indices and the final predictions. This intermediary layer filters out measurement noise and extracts meaningful patterns, thereby improving prediction reliability without requiring higher precision magnetic indices.
2Reliability
If magnetic activity indices are used for forecasting, then warnings can be issued, but the timing is delayed reducing effectiveness
Solution Approach 1:
The patent applies mathematical models and symbolic transformations to magnetic activity data in advance, preparing predicted outcomes before adverse events occur. This preliminary processing enables earlier warning issuance while maintaining reliability, as the symbolic representation system is designed to detect patterns that precede actual GIC events.
Solution Approach 2:
The patent replaces traditional mechanical forecasting methods with mathematical modeling and symbolic computation. This substitution enables faster processing of magnetic activity data, reducing forecast delays while maintaining or improving warning effectiveness through more sophisticated pattern recognition.
3Productivity
If traditional forecasting methods are used, then warnings can be generated, but false warnings cause unnecessary disruptions
Solution Approach 1:
The patent incorporates feedback mechanisms where predicted GIC events are compared with actual events to refine the symbolic representation system. This feedback loop continuously improves warning accuracy by learning from past predictions, thereby reducing false warnings while maintaining power grid productivity through more reliable alerting.
Solution Approach 2:
The patent changes the parameters used in forecasting from direct magnetic index thresholds to symbolic patterns that represent complex relationships in the data. This parameter transformation enables more accurate distinction between true GIC events and false alarms, improving reliability without compromising power grid operational productivity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides more precise predictions of GIC events with a lead time, enabling effective planning to mitigate disruptions by accurately determining the duration and intensity of GIC events, reducing false warnings and improving the accuracy of power grid impact assessments.
Implementation Method 1
Solar eruptions can generate geomagnetically-induced currents (GICs) on exposed overhead transmission lines
Implementation Method 2
one or more remote observatories, where magnetometers are used to record adverse effects due to solar activity on a power grid
Data Source
AI summary
A method of receiving a substorm activity data log from one or more remote sensors, where substorm activity is due to solar activity. The method also includes receiving a harmonic distortion data stream from one or more remote observatories monitoring disturbances on a power grid, where the distortion is due to geomagnetically induced currents. The method also includes applying a mathematical model to the harmonic distortion data stream to create a derived harmonic distortion data log, and comparing phase-to-phase similarity of the three phases from the derived harmonic distortion data log. The method also includes predicting future adverse events due to geomagnetically-induced currents on the power grid based at least in part on the comparison and the substorm activity data log, and creating a warning based on the prediction.