配电网量测数据动态联邦学习框架、自适应隐私保护模型和边缘侧贡献度评估OA

Measurement data,as a crucial operational element for all stakeholders in distribution network management and a cornerstone asset for enterprises,exhibit diverse characteristics such as varying privacy protection requirements among stakeholders and heterogeneous forms of datasets.These characteristics significantly constrain the empowerment potential of measurement data.Federated learning has garnered widespread attention for its ability to address data silo issues.However,traditional federated learning frameworks are plagued by inadequate privacy protection for participant data,decreased model performance due to data heterogeneity,and a lack of effective incentive mechanisms.To tackle these issues,the adaptive privacy-protected dynamic federated learning framework(AP-DFL)is proposed.First,considering the different emphasis of privacy protection for different load types,the sensitivity of the dataset is defined from the two-dimensional perspectives of anonymity and confidentiality.On this basis,the privacy budget for each round of training on the edge side is dynamically adjusted to achieve adaptive local differential perturbation.Then,combined with the global differential perturbation on the main station side,privacy attacks are effectively avoided.Next,a participant contribution assessment model based on matrix decomposition Shapley values is proposed.This model efficiently calculates contribution values through the reconstructed method of value matrix decomposition under sampling.The aggregation weights are adaptively adjusted based on the contribution values to achieve dynamic federated aggregation,thus enhancing the convergence speed of the model under data heterogeneity.Finally,experimental analysis is conducted on this federated learning framework in typical distribution network scenarios,demonstrating its feasibility.