Generative Artificial Intelligence for Panoramic Sensing and Prediction Techniques in Novel Power Distribution Systems

This paper is based on the definition of novel distribution system panoramic perception technology under the perspective of generative artificial intelligence.The preprocessed data are put into forward GRU neurons and reverse GRU neurons as model input variables for multi-task assisted training, and the model outputs distribution system perception results to complete the task of constructing a new distribution system panoramic perception model based on BiGRU.When the distribution system current and voltage data is zero, it will lead to a reduction in the current and voltage prediction accuracy of the distribution system of the ELM model, for this reason, it is proposed to use the genetic algorithm to optimize the ELM model, to achieve the modeling of the new distribution system prediction model based on the ELM-GA algorithm.Using the model constructed in this paper, panoramic perception and prediction analysis of the new distribution system is carried out.When the BiGRU model is deployed in the new distribution system, the BiGRU network's system perception accuracy and error rate are 95.00% and 5.00%, respectively, which fully meets the user experience requirements of the new distribution system, and the relative errors of fault voltage and fault current prediction based on the ELM-GA algorithm for the new distribution system are less than 5%, which indicates that the ELM-GA distribution system prediction model has the characteristics of high robustness and high accuracy.