This article explores the application of optimized wavelet transformation in anomaly detection algorithms and the analysis of digital substation electrical equipment's using autoencoding recurrent neural networks. In the context of the increasing digitalization of electrical power generation, particularly in digital substations, the need for effective equipment monitoring, optimization, and decision support systems is growing. An essential challenge lies in analyzing equipment condition, especially in cases where direct monitoring systems are unavailable. In response to this challenge, the article highlights the potential of autoencoding recurrent neural networks (ARNNs) and optimized wavelet transformation, emphasizing their synergistic capabilities. Additionally, it discusses the advantages of leveraging standardized protocols such as IEC-61850 for efficient data processing. The research results confirm the efficiency of optimized wavelet transformation in anomaly detection and electrical equipment analysis, offering a promising avenue for enhancing the effectiveness and reliability of energy systems. © 2023 IEEE.