Identification of Bad Data in Power Systems Based on Improved Transformer
To improve the identification rate of bad data in power grid state estimation,this paper proposes a novel method based on an improved Transformer model.First,the traditional Transformer encoder structure is enhanced by incorporating a Gaussian kernel function into the self-attention mechanism,enhancing the model's capability to detect neighboring points of bad data.Sec-ond,a loss function is introduced based on a JS divergence maximization-minimization training strategy.This approach achieves a dynamic balance between Gaussian distribution weights and attention weights through mutual optimization in two stages.Utili-zing an unsupervised learning framework,the model is trained with normal measurement data to reconstruct the input,allowing the calculation of reconstruction errors and scores for effective bad data identification.Finally,simulation results demonstrate that the proposed method outperforms existing approaches in terms of precision,recall,F1 score,and overall accuracy,thereby validating its effectiveness in bad data detection for power systems.
identification of bad datatransformer networkunsupervised learninggaussian kernel functionreconstruction score
万方数据
维普
