Open AccessOn‐load tap‐changer fault mode recognition based on the singular value of Hilbert energy spectrum time‐frequency matrix and spectrum entropy
Author(s) -
Geng Jianghai,
Zhang Zikang,
Wang Xinyu,
Gao Shuguo,
Wang Ping
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12519
Subject(s) - hilbert–huang transform , vibration , singular value , singular value decomposition , entropy (arrow of time) , mathematics , hilbert transform , eigenvalues and eigenvectors , time–frequency analysis , control theory (sociology) , energy (signal processing) , algorithm , pattern recognition (psychology) , computer science , mathematical analysis , artificial intelligence , acoustics , spectral density , physics , statistics , quantum mechanics , control (management) , telecommunications , radar
To solve the difficulty of eigenvalue extraction of on‐load tap‐changer (OLTC) vibration signals, an eigenvalue extraction method of time‐frequency matrix based on Hilbert energy spectrum and spectral entropy is proposed in this study. By optimizing the Hilbert‐Huang transform (HHT) algorithm, the Hilbert energy spectrum of the vibration signal based on the time‐frequency distribution is obtained. The spectrum entropy of Hilbert energy spectrum was extracted to characterize the chaotic degree of vibration signal energy distribution, and avoided the problem of inaccurate feature quantity such as similarity due to the minute time difference during signal acquisition. A new time‐frequency matrix was constructed by frequency band division and singular value decomposition (SVD) was carried out. The singular value vector obtained contains the time, frequency and energy information, such as vibration frequency distribution and time‐frequency plane energy distribution, which represent the essential features of the original signal. The mechanical states of OLTC were characterized by Hilbert spectrum entropy and singular value. The features of the measured signals under the three typical faults of the driving mechanism are input into the intelligent multi‐classification support vector machine (DAG‐SVM) for pattern classification. The results show that this method can accurately identify the mechanical state of OLTC, and has practical application significance.