TY - GEN

T1 - Application of Optimized Wavelet Transformation for Analysis of Digital Substation Electrical Equipment Operating Modes

T2 - 2023 Belarusian-Ural-Siberian Smart Energy Conference (BUSSEC)

AU - Tronin, Artem

AU - Eroshenko, Stanislav

AU - Efimov, Alexander

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85178024184

U2 - 10.1109/BUSSEC59406.2023.10296281

DO - 10.1109/BUSSEC59406.2023.10296281

M3 - Conference contribution

SN - 979-835035807-0

SP - 79

EP - 83

BT - Proceedings of the 2023 Belarusian-Ural-Siberian Smart Energy Conference, BUSSEC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 25 September 2023 through 29 September 2023

ER -