Xiu Liu, Liang Gu*, Xin Gong, Long An, Xurui Gao, Juying Wu
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4045-4061, 2024, DOI:10.32604/cmc.2024.048442
Abstract 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… More >
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