Acoustic Imaging based Covariance Matrix Fitting Algorithm for
Transformer Fault Diagnosis
Abstract
With the vigorous development of power system, fault diagnosis of power
transformers is very important to ensure the normal operation of the
power system. However, the traditional transformer monitoring methods
have blind spots, and the diagnosis results depend heavily on the
experience of the managers. At present, it is urgent to develop new
technologies to improve the accuracy of transformer fault diagnosis. How
to diagnose transformer faults quickly, accurately, and effectively has
become a difficult problem. Encouragingly, acoustic imaging, as a visual
technology of sound field, promotes the development of transformer fault
diagnosis. In this paper, the principles and technical status of
acoustic imaging are summarized. Meanwhile, the covariance matrix
fitting (CMF) beamforming algorithm is compared with the traditional
beamforming algorithm, and the main factors affecting the performance of
the array beamforming algorithm are simulated and analyzed. Finally, an
acoustic imaging technology for transformer fault diagnosis based on CMF
algorithm is proposed. This technology can accurately diagnose the
transformer fault state according to the distribution of the internal
sound field of the transformer, improve the efficiency of fault
diagnosis, and promote the construction of smart grid. Through the
analysis and processing of the acoustic test data of seven power
transformers with different voltage levels and loads, including 500kV
Yanshan, 220kV Tinghu and Laoshan transformers in Wenshan Power Supply
Bureau, the test results prove that the acoustic imaging technology
based on CMF algorithm can accurately and conveniently diagnose
transformer faults.