Transformer working state identification method based on voiceprint signal-motif difference field enhancement and multi-head self-attention mechanism
In order to improve the intelligent monitoring level of power transformer working state,a method of transformer working state identification based on motif difference field(MDF)voiceprint signal enhancement and multi-head self-attention mechanism is proposed.Based on the MDF technology,the sound signal is mapped into a two-dimensional image,and then the depth mining and state recognition of image information are realized with the help of the visual converter of multi-head attention mechanism,and the explanatory power analysis of classification results is realized by using gradient weighted class activation mapping.The experimental simulation test system platform containing four typical operating states of the transformer is constructed,and the experimental results show that the proposed method not only can effectively characterize the state characteristics of the transformer acoustic signal,but also has a higher classification accuracy compared with the"time-frequency+transformer network with multi-head self-attention mechanism"and the"MDF+visual converter with multi-head attention mechanism",which is about 6％,and also has better robustness,which can provide a certain reference for the research on the detection of faults in electrical equipment.
NETL
NSTL
万方数据
维普
