A time mixer-based regional power grid short-time voltage disturbance dominant cause identification method

By employing the TimeMixer method to perform time alignment and multi-scale feature extraction on multi-channel time-series data of power grid disturbances, combined with counterfactual condition analysis, the problem of locating the source of power grid disturbances was solved, enabling accurate location and quantitative assessment of power grid faults and improving the objectivity and consistency of the analysis.

CN122153830BActive Publication Date: 2026-07-24CHANGSHA RIOTTO ELECTRONIC TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHANGSHA RIOTTO ELECTRONIC TECHNOLOGY CO LTD
Filing Date
2026-05-11
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately pinpoint the source of power grid disturbances. Traditional methods rely on experience-based judgment, which cannot effectively handle complex events involving cascading operations of multiple devices. Furthermore, they lack assessment of the causal relationship between electrical quantity fluctuations and device actions, leading to analysis results that depend on personal experience and are prone to bias.

Method used

Using a TimeMixer-based approach, this study identifies voltage change boundary moments by aligning and fusing multi-channel time-series data, combining multi-scale hybrid encoders and counterfactual conditional analysis, calculating intervention effect scores, quantitatively assessing the influence of each factor, and ultimately identifying the dominant inducing factor.

Benefits of technology

It enables precise location of short-term voltage disturbances in the power grid, eliminates errors introduced by data asynchrony, improves the objectivity and consistency of analysis, provides multi-dimensional disturbance assessment, and supports differentiated processing strategies.

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Abstract

The present application belongs to the technical field of intelligent monitoring of power system, and discloses a regional power grid short-time voltage disturbance leading cause identification method based on TimeMixer. Firstly, the multi-channel time sequence data is unified through the maximum common sampling period, the voltage change boundary time is accurately identified, and the change segment division and position marking are performed on the disturbance chain time sequence data. Then, the change segment features are extracted from three time scales of coarse, medium and fine by using the TimeMixer++ multi-scale hybrid encoder, and the intervention effect of each leading segment on the voltage disturbance is evaluated by using the counterfactual conditional modeling method. Finally, the leading cause object is determined based on the intervention effect score and the time sequence constraint. The present application realizes the accurate quantification of the voltage disturbance causal relationship and the automatic identification of the leading cause, breaks through the limitation of traditional experience judgment, and provides reliable technical support for power grid fault analysis, predictive maintenance and operation optimization.
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