基于因果推断双特征融合的非高损线路窃电识别

doi:10.19595/j.cnki.1000-6753.tces.251255

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Abstract User power theft is the main cause of abnormally high losses on voltage lines in distribution networks. With the improvement of power grid inspection, the effect of power theft control on high-loss lines is remarkable. However, non-high-loss lines still exist in electricity theft by users, the existence of such users to steal electricity is relatively small, easy to be affected by the normal fluctuations in electricity consumption and external noise, caused by the anomalous fluctuations of the daily average line loss in the blurring of the characteristics of the line loss is relatively hidden and difficult to find. Aiming at the problems of hidden behavior of power theft in non-high-loss scenarios of medium-voltage (MV) lines, easy to confuse the differential features caused by power theft, and limited recognition accuracy of traditional detection techniques, a method for detecting power theft in non-high-loss lines is proposed by integrating the causal inference of line loss and the cyclic load features. The core of the approach involves constructing a hybrid graph-based model that synergistically combines a temporal feature extraction network with a causality-driven graph attention mechanism.
First, by constructing WaveNet, load sequences with different cycle lengths are divided to capture the load features of different cycles and fused into the node feature matrix of the model. Second, the graph attention neural network based on causal inference is established to determine the directional connection relationship between each node and line loss, and the causal correlation between user behavior and line loss is mined by using causal sampling, which effectively improves the anti-interference ability of the model, and further calculates multi-attention coefficients to obtain the domain information in the causal paths, and constructs the adjacency matrix. Finally, the graph network structure is used to undertake two kinds of electricity behavior characteristics and refine the contribution of each user to the abnormal fluctuation of line loss. By simulating different types of power theft behavior with examples and analysis, the experimental results show that the model proposed has good detection accuracy and robustness.
Simulation experiments on line non-high-loss power theft scenarios were conducted using actual collected user data. The experimental results show that the proposed model analyzes the non-high-loss scenarios of single-user power theft, and its detection indexes are all above 95%, while the recognition accuracy A_cc for average power theft, peak-shaving power theft, and identification of zero power theft is close to 99%. In various non-high-loss scenarios of mixed multi-user power theft, the detection indexes of the proposed model are all over 94%, of which the recognition accuracy A_cc is close to 96%, which proves the effectiveness of the proposed model. Through the perturbation experiment analysis, it can be seen that under 20% perturbation, combining the C-GAT model to analyze the non-high-loss scenarios, the evaluation indexes are still higher than 90%, which has strong anti-interference ability. In the ablation experiment, comparing the detection performance of multiple models verifies that the proposed dual-module cooperative power theft detection model has good recognition effect in non-high-loss scenarios.
The following conclusions can be obtained from the analysis: (1) The analysis of the user's small amount of electricity theft causes the 15 min scale line loss to show a local high loss state, but the phenomenon of the daily scale full-volume line loss blurring masks the characteristics of the theft of electricity. (2) The model proposed can effectively distill the causal connection between the user's longitudinal cycle law and the abnormal line loss caused by electricity theft, and combine the two characteristics to evaluate the extent of the user's behavioral abnormalities. (3) The model proposed has a strong robustness and anti-jamming capability in different electricity theft scenarios, and it is more resistant to interference.

Key words： Electricity theft detection causal inference line loss analysis anomaly detection neural network

Received: 15 July 2025

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	Xie Yaoting
	Zhou Wenqing
	Su Sheng
	Li Bin
	Xiang Sheng

Cite this article:

Xie Yaoting,Zhou Wenqing,Su Sheng等. Non-High-Loss Line Power Theft Identification with Causal Inference Dual Feature Fusion[J]. Transactions of China Electrotechnical Society, 2026, 41(15): 5191-5205.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.251255 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I15/5191

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