Anomaly Detection for Smart Grid Information Data Considering Sample Imbalance Using Improved AlexNet

In smart grid operation, the scarcity of abnormal samples causes data imbalance, which is a key factor limiting the accuracy of anomaly detection. To address this issue and simultaneously solve the problem of easily losing weak abnormal signals in one-dimensional time-series data, an abnormal data detection method for grid information using an improved AlexNet considering sample imbalance is proposed. Firstly, features like voltage, current, and power are extracted from historical data. Missing values are filled via Lagrange interpolation, and abnormal boundaries are determined using box plots to construct high-quality samples. Secondly, to address the problem of few abnormal samples and imbalanced distribution, an enhanced learning strategy combining time-series translation and Gaussian noise injection is adopted to expand the abnormal samples and obtain sufficient training data. Then, to preserve the integrity of weak signals in one-dimensional time-series data and amplify the differences in abnormal features, the Gram angle field is used to convert multi-dimensional time-series data into a two-dimensional image, achieving the visual representation of time-series features. Finally, combined with the powerful image detection capability of AlexNet, it is improved by lightweighting the network structure, introducing the multi-head self-attention mechanism, and optimizing the training strategy to adapt to abnormal detection in the small sample and imbalanced environment of the grid. The simulation experiments show that the proposed method achieves an accuracy rate of 91.32% on extremely imbalanced datasets, which is at least 3.1% higher than those of other models.