Modal Identification of External Vibration Signals of Transmission Lines Based on TDM-MIMO Millimeter Wave Radar

After external impact, the frequency component of the vibration signal undergoes transient attenuation, which may be mixed with low-frequency electromagnetic interference, leading to misjudgment. To this end, a modal identification method for transmission line vibration signals based on TDM-MIMO radar is proposed. High quality signals are captured using sparse transmit and dense receive antenna arrays, decomposed into multiple eigenmode function components, and classified using an extremum learning machine. Experimental results have shown that this method achieves an accuracy of 99.04% in separating frequency components and identifying vibration modes, which is accurate and effective.

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Published
2025-03-21
DOI
https://doi.org/10.1109/sgai64825.2025.11009699
Primary Topic
Power Systems Fault Detection
Type
article
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article

Modal Identification of External Vibration Signals of Transmission Lines Based on TDM-MIMO Millimeter Wave Radar

Songnong Li, Wenxin Peng, Li Cai
Power Systems Fault Detection
article

Modal Identification of External Vibration Signals of Transmission Lines Based on TDM-MIMO Millimeter Wave Radar

Songnong Li, Wenxin Peng, Li Cai
article en

Abstract

After external impact, the frequency component of the vibration signal undergoes transient attenuation, which may be mixed with low-frequency electromagnetic interference, leading to misjudgment. To this end, a modal identification method for transmission line vibration signals based on TDM-MIMO radar is proposed. High quality signals are captured using sparse transmit and dense receive antenna arrays, decomposed into multiple eigenmode function components, and classified using an extremum learning machine. Experimental results have shown that this method achieves an accuracy of 99.04% in separating frequency components and identifying vibration modes, which is accurate and effective.

North China Electric Power University (CN), China Electric Power Research Institute
Affordable and clean energy
Openalex Percentile: Top 87%
Power Systems Fault Detection
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