Security analysis method of power monitoring system based on big data technology

Fuchuan Zhang; Chuyang Zeng; Siyao Yi; Xiaojian Pang; Haiguang Chen

doi:10.1117/12.2641234

19 October 2022 Security analysis method of power monitoring system based on big data technology

Fuchuan Zhang, Chuyang Zeng, Siyao Yi, Xiaojian Pang, Haiguang Chen

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122940H (2022) https://doi.org/10.1117/12.2641234
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

Power monitoring system can monitor the operation of power system, but there are still defects in analysis and early warning. Most of the current safety analysis methods rely on a single dimension for anomaly analysis and early warning, resulting in large deviations in analysis results and low alarm reduction rate. In order to solve the above problems, the security analysis method of power monitoring system based on big data technology is studied. Spark technology is used to establish a big data framework for security analysis of power monitoring system, and the communication link data of power monitoring system is analyzed from the perspective of data statistics and abnormal characteristics. Deep neural network in machine learning is used to analyze the abnormal analysis results and realize effective power monitoring and early warning. Through the comparison test of an example, it is verified that the prediction accuracy of this security analysis method is higher than 94.8%, and the alarm reduction rate is significantly improved, which has good practical application value.

Citation Download Citation

Fuchuan Zhang, Chuyang Zeng, Siyao Yi, Xiaojian Pang, and Haiguang Chen "Security analysis method of power monitoring system based on big data technology", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122940H (19 October 2022); https://doi.org/10.1117/12.2641234

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