An identification method based on MTF visualization and improved DenseNet for power quality disturbances
Aiming at the problems of complex process and insufficient refinement of artificial feature selection in traditional power quality disturbances (PQDs) classifier,a new PQD recognition method based on Markov transition field visualization and improved DenseNet is proposed. Firstly,the one-dimensional PQD signal is mapped into a two-dimensional image by MTF. Then,the image is input into an improved DenseNet with a new channel attention mechanism. Finally,the network is trained to extract features from a large number of samples by itself,so as to realize the correct recognition of PQD signals. The example results show that:in the case of no noise and signal-to-noise ratio of 20dB and 30dB,the proposed improved DenseNet can effectively overcome the shortcomings of traditional methods,such as strong subjectivity of feature selection and poor anti-noise performance. It can better extract the feature information of complex PQD,and has a high recognition rate for complex PQD.
power quality disturbanceMarkov translate fieldvisualizationdense convolutional networkschannel attention mechanismclassification and recognition
万方数据
维普
