Abstract: With the development of big data, the Internet of Things, cloud computing, and artificial intelligence technologies, this paper proposes an online monitoring and state evaluation method based on these technologies to improve the accuracy, reliability, and economy of power systems. Traditional electromagnetic current transformers require offline calibration with power outages, making it difficult to reflect their actual operating status, increasing operational complexity and costs, and affecting the accuracy of energy metering and the stability of the power grid. Addressing the issue of the lack of a standard transformer as a reference for online error monitoring of current transformers, this paper proposes an online monitoring method based on the Gated Attention Transformer model (Gatedformer). This method uses multiple current data inputs, leverages the Gatedformer model to learn data features, and accurately predicts future standard values of current transformers. Specifically, the method embeds time point variables through the attention mechanism to capture multivariable correlations; it uses a gated attention network to capture the long-term dependency characteristics of time series and applies a feedforward network to learn nonlinear representations. Experimental results show that under the condition of predicting the standard current value window for the next day, the model achieves a prediction error of only 0.090% in the online monitoring of current transformers, outperforming existing models.