A Method and System for Detecting Energy Storage Charging Anomalies Based on User Switching Behavior

By using a user-based energy storage charging anomaly detection method based on user switching behavior, and by employing a reconstructed autoencoder model and contribution analysis, the problem of the inability to identify soft anomalies in energy storage systems in existing technologies is solved, enabling precise monitoring and root cause localization of the interaction between energy storage devices and the power grid.

CN121765604BActive Publication Date: 2026-05-26STATE GRID ZHEJIANG ELECTRIC POWER CO LTD JINHUA POWER SUPPLY CO +2
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
STATE GRID ZHEJIANG ELECTRIC POWER CO LTD JINHUA POWER SUPPLY CO
Filing Date
2026-02-28
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies cannot effectively identify soft anomalies during the charging process of energy storage systems. They rely on anomaly tags and have weak generalization ability. Traditional methods cannot adapt to nonlinearity and dynamics, and lack interpretable anomaly detection methods.

Method used

The energy storage charging anomaly detection method based on user switching behavior acquires electricity consumption time-series data, uses a dual-threshold hysteresis comparison mechanism to identify charging segments, and extracts basic state features, strategy template compliance features, and behavioral profile compliance features to construct a reconstructed autoencoder model for anomaly detection. The root cause of the anomaly is determined by combining contribution analysis.

Benefits of technology

It enables comprehensive monitoring of the interaction between energy storage devices and the power grid, accurately detects various soft anomalies, reduces reliance on anomaly tags, and improves generalization capabilities and operation and maintenance efficiency.

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Abstract

This invention discloses a method and system for detecting energy storage charging anomalies based on user switching behavior, belonging to the field of energy storage anomaly identification technology. Addressing the problems of existing technologies that only focus on the physical state of equipment, cannot cover soft anomalies, rely on fault labels, have weak generalization ability, and lack interpretability, this application identifies effective charging segments from electricity consumption time-series data through a dual-threshold hysteresis comparison mechanism. Then, it extracts basic state features, strategy template compliance features, and behavior profile compliance features from the effective charging segments to construct a high-dimensional vector. This high-dimensional vector is input into a reconstruction autoencoder model trained on normal data. The anomaly degree is calculated through the reconstruction residual. Based on the contribution of each feature factor in the input vector to the anomaly degree, a fault index is performed using a rule base to determine the root cause of the fault. This achieves accurate detection and interpretable diagnosis of multiple types of soft anomalies in energy storage charging, eliminating the need for fault labels and significantly improving anomaly detection efficiency and accuracy.
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