电力系统保护与控制2025，Vol.53Issue(24)：111-120,10.DOI:10.19783/j.cnki.pspc.250113
考虑数据隐私保护的分布式电源集群自适应可信协同决策方法
Adaptive and trustworthy collaborative decision-making for distributed generator clusters considering data privacy protection
摘要
Abstract
Rapid fluctuations of large-scale distributed generation(DG)can easily cause issues such as voltage violation in distribution networks,affecting their safe and stable operation.Additionally,distribution clusters may belong to different stakeholders,making inter-cluster data privacy increasingly important.To address the problem of DG cluster control under multiple stakeholders,an adaptive and trustworthy collaborative decision-making method that considers data privacy protection is proposed.First,a multi-cluster trusted collaborative framework for distribution networks is constructed based on split federated learning approach,and a deep learning model is established for voltage decision-making.This framework enables data fusion and collaboration among multiple clusters with privacy protection between stakeholders.Then,a reward mechanism is incorporated into the deep learning model to evaluate the quality of measurement data,allowing for adaptive model updates.Finally,the feasibility and effectiveness of the proposed method are verified using a case study of the Jiaomen distribution network in Guangzhou.The results demonstrate that the proposed method offers strong privacy protection capabilities,improves voltage quality,and effectively mitigates voltage violation issues in the distribution network.关键词
配电网/集群控制/分布式电源/自适应控制/隐私保护Key words
distribution network/cluster control/distributed generation/adaptive control/privacy protection引用本文复制引用
崔婧琪,冀浩然,李鹏,喻磊,段舒尹,雷一勇..考虑数据隐私保护的分布式电源集群自适应可信协同决策方法[J].电力系统保护与控制,2025,53(24):111-120,10.基金项目
This work is supported by the National Natural Science Foundation of China(No.U22B20114). 国家自然科学基金项目资助(U22B20114) （No.U22B20114）
南方电网公司科技项目资助(ZBKJXM20232291) （ZBKJXM20232291）
