A Power Data Anomaly Detection Model Based on Deep Learning with Adaptive Feature Fusion

With the popularisation of intelligent power, power devices have different shapes, numbers and specifications. This means that the power data has distributional variability, the model learning process cannot achieve sufficient extraction of data features, which seriously affects the accuracy and performance of anomaly detection. Therefore, this paper proposes a deep learning-based anomaly detection model for power data, which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction. Aiming at the distribution variability of power data, this paper developed a sliding window-based data adjustment method for this model, which solves the problem of high-dimensional feature noise and low-dimensional missing data. To address the problem of insufficient feature fusion, an adaptive feature fusion method based on feature dimension reduction and dictionary learning is proposed to improve the anomaly data detection accuracy of the model. In order to verify the effectiveness of the proposed method, we conducted effectiveness comparisons through elimination experiments. The experimental results show that compared with the traditional anomaly detection methods, the method proposed in this paper not only has an advantage in model accuracy, but also reduces the amount of parameter calculation of the model in the process of feature matching and improves the detection speed.