中国电机工程学报2026，Vol.46Issue(11)：4416-4427,中插5,13.DOI:10.13334/j.0258-8013.pcsee.251124
基于自监督学习的配电网分布式最优潮流求解
A Distributed Optimal Power Flow Solving Method for Distributed Network Based on Self-supervised Learning
摘要
Abstract
With the increasing complexity of grid structures,the distribution network has become a necessary means to improve grid scheduling efficiency,reliability,and economy.However,traditional distributed convex optimization algorithms are gradually losing the ability to meet the efficient operation requirements.In this context,machine learning has gained increasing attention.But the requirement for a large number of labeled samples has limited the practical application of machine learning.Aiming at this limitation,this paper proposes a self-supervised learning algorithm integrating the alternating direction method of multipliers(ADMM)to solve the distributed optimal power flow for a distributed network.First,two independent neural networks are constructed to simulate the interactive iteration of global variables and Lagrange multipliers in the ADMM,achieving mutual supervision between networks.The primal network is used to estimate global variables,while the dual network is employed to estimate Lagrange multipliers.Then,this paper proposes a loss function considering consistency constraints to guide the training of the primal network and ensure the convergence of the training process.Third,the convergence of the proposed method is analyzed and proven based on the Karush-Kuhn-Tucker(KKT)conditions,and the upper bound of the residual is derived.Finally,this paper conducts simulation experiments on the modified IEEE 123 bus system and the actual 276 bus distribution network in Suzhou to verify the effectiveness of the proposed method.关键词
分布式最优潮流/自监督学习/交替方向乘子算法/一致性约束/卡罗需-库恩-塔克条件Key words
distributed optimal power flow/self-supervised learning/alternating direction method of multipliers/consistency constraints/Karush-Kuhn-Tucker conditions分类
信息技术与安全科学引用本文复制引用
龙寰,蔡辉煌,张晓,吴志,顾伟..基于自监督学习的配电网分布式最优潮流求解[J].中国电机工程学报,2026,46(11):4416-4427,中插5,13.基金项目
江苏省碳达峰碳中和科技创新专项(产业前瞻与关键核心技术攻关)(BE2023093-2) （产业前瞻与关键核心技术攻关）
国家自然科学基金项目(62572116) （62572116）
中国科协青年人才托举工程(2023QNRC001).Jiangsu Industry Outlook and Key Technology Research Project Program(BE2023093-2) （2023QNRC001）
Project Supported by National Natural Science Foundation of China(62572116) （62572116）
Young Elite Scientists Sponsorship Program by CAST(2023QNRC001). （2023QNRC001）
