Branch parameter identification based on convolution Transformer
In order to solve the problem that the traditional single-branch feature parameter identification can only identify a single target,but cannot make full use of the historical information of the power system.Based on the Transformer model,a convolution self-attention is used to help the model better capture the re-lationship between input features and better incorporate the local context into the attention mechanism.At the same time,the convolution gating loop unit is used for position coding to ensure the consistency of con-tent and position,thus reducing the training loss and further improving the prediction accuracy.The simula-tion results show that the prediction accuracy of this algorithm is higher than other machine learning algo-rithms and deep learning algorithms.
deep learningconvolution self-attentionrelative position coding strategyparameter identifi-cationtransmission lines
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