Transformer Status Recognition Based on Dual Microphones and Deep Learning
To overcome the shortcomings of single-sensor recognition algorithms with missed and false detections,this paper proposes an algorithm based on multi-sensor data fusion and deep learning,namely,1D-CNN-based dual microphones fusion algorithm(1D-CNN-DMF algorithm for short).The algorithm utilizes dual microphones to simultaneously collect acoustic signals from transformer statuses.A one-dimensional convolutional neural network is used to extract features from the acoustic signals collected by each microphone.The extracted features are then fused using a fully connected layer,and the output features are classified by a softmax classifier.The algorithm is validated using a dataset of acoustic signals collected from a 500kV transformer in four different statuses.The experimental results demonstrate that the proposed algorithm effectively recognizes different transformer statuses,and the accuracy of 1D-CNN-DMF algorithm is higher than 1D-CNN-LSTM,1D-CNN,FFT-BP,SVM and FFT-SAE.Furthermore,the t-SNE visualization tool is used to reveal the inner mechanism of the proposed algorithm.
deep learningstatus recognitionacoustic signal processingconvolutional neural networks
万方数据
维普
