Research on the Optimization of Association Rule Mining Algorithm for Line Loss Anomaly Diagnosis

Abnormal line loss in distribution networks directly impacts grid operational efficiency. Traditional diagnostic methods struggle with the high-dimensional, multi-source massive line loss data. To address this, this study proposes an optimized association rule mining algorithm for line loss anomaly diagnosis. By introducing business domain feature constraints and optimizing weighted support and confidence metrics, the association rule mining algorithm reduces computational overhead and rule redundancy. Combined with rule matching deviation and neighborhood similarity, an outlier identification mechanism is constructed to capture hidden anomalies. Finally, a regression-based diagnostic model is established by integrating multi-source features to achieve graded diagnosis of anomaly severity. Experiments based on 180 days of measured data from a city's distribution transformer areas show that this method can effectively identify anomalies such as load mutation, metering deviation, and electricity theft, improving diagnostic accuracy and efficiency, and providing data support for the lean management of distribution networks.