Electric power abnormal fluctuation detection method and system fused with time sequence modeling

By integrating time series modeling and fully connected networks to generate adaptive dynamic threshold ranges, the problems of industry differences and time misalignment in power anomaly detection are solved, enabling precise anomaly monitoring and forward-looking early warning of macro-industry power consumption, and reducing system maintenance costs.

CN121682584APending Publication Date: 2026-03-17国网福建省电力有限公司营销服务中心 +1
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Patent Information

Application Number
CN202511826684.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-05
Publication Date
2026-03-17

AI Technical Summary

Technical Problem

Existing technologies rely on manually setting fixed thresholds for detecting abnormal power fluctuations. This fails to consider the differences in electricity consumption characteristics across different industries, making it difficult to adapt to changes in power consumption patterns. Consequently, false alarms and missed alarms occur frequently, and there is a lack of mechanisms to handle the impact of special events and time misalignments.

Method used

A time series modeling approach is adopted, which constructs a time series model through the L-BFGS quasi-Newton method and combines it with a fully connected network to generate an adaptive dynamic threshold range. Considering industry characteristics and external risks, a CNN-LSTM-Attention architecture is used for short-term prediction, and the threshold is dynamically adjusted to adapt to changes in electricity consumption patterns.

Benefits of technology

It enables precise anomaly monitoring of power consumption across macro-industries, reduces false alarm and missed alarm rates, improves the foresight of power fluctuation detection and system maintenance costs, and has cross-industry generalization capabilities.

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Abstract

The invention discloses an abnormal power fluctuation detection method and system fused with time sequence modeling. The method comprises the following steps: acquiring daily electric quantity year-on-year data of each industry and an industry power consumption data set; the method comprises the following steps: constructing a time sequence model based on year-on-year data of daily electric quantity of each industry, calculating posterior distribution of a predicted value of the time sequence model through an L-BFGS quasi-Newton method, and calculating a data basic confidence interval of a next time point; inputting each characteristic value of the time sequence model one day before the date to be predicted, the mean absolute error of the predicted value of the short-term historical window time sequence model, the residual standard deviation and the industry power consumption data set into a full-connection network to obtain a heteroscedasticity confidence interval; and calculating a final threshold interval based on the two confidence intervals to carry out industry electricity consumption abnormity judgment. According to the method, the problem that the phenomena of false alarm and missing report occur frequently due to the fact that the power utilization characteristic difference between different industries is not considered and the change of the electric quantity rule along with time is difficult to adapt in the prior art is solved.
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