沈阳工业大学学报2026，Vol.48Issue(3)：1-8,8.DOI:10.7688/j.issn.1000-1646.2026.03.01
基于决策树和多代理系统的配电主站故障自愈方法
Fault self-healing method for distribution main stations based on decision tree and multi-agent system
摘要
Abstract
[Objective]Due to the low fault localization accuracy and efficiency of traditional fault self-healing methods,a fault self-healing method for distribution main stations based on the decision tree and multi-agent system(MAS)was proposed to improve the fault handling capability of distribution systems.[Methods]A hierarchical multi-agent technology was adopted to construct a fault self-healing system for distribution main stations,which included the feeder agent and node area agent.The distribution network data were collected and the gradient boosting decision tree(GBDT)algorithm was employed in the node area agent to complete fault localization,and the fault data were transmitted to the feeder agent.In the feeder agent,data were summarized,the influence of important load recovery sequence,transfer margin,and line loss was comprehensively considered to build a fault self-healing optimization model,and the model was solved via the multi-agent evolutionary algorithm to obtain the optimal fault self-healing recovery scheme for distribution main stations.[Results]Based on the IEEE-29 system,experimental analysis was conducted on the proposed method,and the results show that the accuracy of the GBDT fault localization algorithm is nearly 97％after 150 iteration.The important load recovery amount,network loss,transfer capacity margin,and fault self-healing time of this method are 100％,90.58 kW,11.26 kW,and 2.79 s respectively.The self-healing recovery rate exceeds 91％,and the highest self-healing control operation complexity is no more than 5,all of which are superior to other comparative methods.[Conclusions]The GBDT fault localization algorithm can achieve more ideal accuracy and efficiency,and the proposed method can recover all important loads in the shortest time,ensuring minimal network loss.Additionally,the proposed method has relatively stable self-healing ability,which can better coordinate new energy generation,quickly adapt to the rapid development of new power systems,and achieve high-quality power supply.Aiming at traditional fault self-healing methods suffering from problems such as large workload and poor accuracy caused by centralized processing modes,the proposed method constructed a fault self-healing system for distribution main stations based on MAS,achieving fast and accurate fault detection and recovery via the distributed collaboration of the operating status of each node.Compared to the decision tree algorithm,the GBDT algorithm gradually improves analysis accuracy by fitting the residuals of the previous round in each round of iteration to construct a new learner.It is applicable to fault localization at the level of distribution main stations and provides accurate data support for fault self-healing.Compared with traditional optimization methods,the GBDT algorithm adopts the multi-agent evolutionary algorithm to solve the fault self-healing optimization model.By assigning the target to each agent for execution,the optimization efficiency is improved,and the excellent solutions of all agents are summarized to obtain the final solution,ensuring the global optimal effect.关键词
配电主站/故障自愈/梯度提升决策树算法/多代理系统/多目标优化模型/馈线代理层/节点区域代理层/多agent演化算法Key words
distribution main station/fault self-healing/gradient boosting decision tree algorithm/multi-agent system/multi-objective optimization model/feeder agent/node area agent/multi-agent evolutionary algorithm分类
信息技术与安全科学引用本文复制引用
周宇晴..基于决策树和多代理系统的配电主站故障自愈方法[J].沈阳工业大学学报,2026,48(3):1-8,8.基金项目
重庆市重点科技项目(SGCQ0000DKJS2310235). （SGCQ0000DKJS2310235）
