This method improves voltage anomaly identification in distribution networks, suggesting enhanced accuracy over clustering and isolation forest algorithms.
Accurate voltage anomaly detection is the prerequisite for the reliable operation of the distribution network. However, the traditional detection methods are prone to missed and false alarms. In practice, the distribution of phase voltage difference satisfies a normal distribution during normal operation and deviates from the distribution during faults. This paper proposes a novel method for voltage anomaly detection in distribution networks based on an improved standard deviation filter. The proposed method identifies an anomaly by evaluating the dispersion degree based on the mean and standard deviation of the phase voltage difference dataset. The short-term cycle, long-term cycle, and weighting coefficient are adopted rationally, thus large data storage requirements and repeated calculations can be avoided. Compared with the clustering and the isolation forest algorithms, the proposed method can identify voltage anomalies more accurately. The reliability of the proposed method is verified by on-site data.
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Chen et al. (2025) studied this question.