Research and prospects of self-healing control methods and their applications in metro power supply systems

doi:10.3969/j.issn.2097-0706.2025.04.004

Abstract

Abstract:

Metro is a crucial part of urban public transportation， and the stability and reliability of its power supply systems are of paramount importance. Unlike traditional high-voltage transmission networks， metro power supply systems operate within a closed AC-DC hybrid network， facing unique challenges such as highly dynamic load variations and short but densely distributed power supply links. Self-healing control ensures safe and continuous operation in the event of faults through real-time monitoring， intelligent diagnosis， and automatic recovery. Research progress on self-healing control methods for power supply systems is reviewed， with a focus on control architectures， intelligent optimization， and fault diagnosis methods. Based on the characteristics of metro power supply systems， the applications of multi-agent systems （MAS） and the IEC 61850 communication standard in self-healing control are explored. Given the trend of integrating distributed energy sources into metro power supply systems， a series of self-healing control strategies are proposed to effectively reduce dependence on centralized power supply. Additionally， emerging research directions based on big data， intelligent algorithms， and distributed control are discussed， providing key technical support for self-healing control in metro power supply systems.

Key words: metro power supply system, self-healing control, multi-agent system, IEC 61850, fault diagnosis, intelligent optimization, artificial intelligence, big data, distributed energy resources

CLC Number:

TK01：TM732

FENG Jianbing, YU Tao, CHENG Lefeng. Research and prospects of self-healing control methods and their applications in metro power supply systems[J]. Integrated Intelligent Energy, 2025, 47(4): 41-62.

Figures/Tables 9

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Table 2

Objective functions and constraints for distribution network fault recovery

项目		函数	特征和复杂度
目标函数	负荷损失最小	$\begin{aligned}\min L= & \lambda_{1} \sum_{i=1}^{N_{1}} z_{i} L_{1 i}+ \\& \lambda_{2} \sum_{j=1}^{N_{2}} z_{j} L_{2 j}\end{aligned}$	λ₁,λ₂分别为一级、二级负载权重系数； z_i,z_j分别为第i个一级负载和第j个二级负载的状态变量
	开关动作最少	$m i n S = ∑ i = 1 N z i$	z_i=1为开关变化； z_i=0为开关不变
	网损最小	$m i n P = ∑ i = 1 N R i P i 2 + Q i 2 U i 2$	R_i,P_i,Q_i,U_i分别为支路i的阻抗、有功功率、无功功率、电压； N为系统支路数
约束条件	支路容量	$P i ≤ P i, m a x$	max为上限； min为下限； g为恢复拓扑； G为辐射拓扑集
	变压器容量	$P t ≤ P t, m a x$
	节点电压	$V i, m i n ≤ V i ≤ V i, m a x$
	节点电流	$I i ≤ I i, m a x$
	辐射结构	$g ∈ G$

Table 2

Fig.6

Fig.7

References 110

[1]	中国城市轨道交通协会. 城市轨道交通2023年度统计和分析报告[EB/OL].(2024-03-29)[2024-10-01]. https://www.camet.org.cn.
[2]	秦博宇, 王宏振, 王召健, 等. 地下空间支撑下的城市轨道交通和能源系统融合发展研究[J]. 中国工程科学, 2023, 25(1): 45-59. doi: 10.15302/J-SSCAE-2023.01.014
	QIN Boyu, WANG Hongzhen, WANG Zhaojian, et al. Integrated development of urban rail transit and energy systems supported by underground space[J]. Strategic Study of CAE, 2023, 25(1): 45-59. doi: 10.15302/J-SSCAE-2023.01.014
[3]	杜芳. 地铁机车建模及直流牵引供电系统故障分析[D]. 北京: 北京交通大学, 2010.
	DU Fang. Modeling of metro locomotive and fault analysis of DC traction power supply system[D]. Beijing: Beijing Jiaotong University, 2010.
[4]	宋晓明. 城市轨道交通牵引供电系统故障定位研究[D]. 北京: 北京交通大学, 2015.
	SONG Xiaoming. Research on fault location of traction power supply system in urban rail transit[D]. Beijing: Beijing Jiaotong University, 2015.
[5]	张润凡, 别朝红. 主动配电网的动态虚拟微电网群分布式群级协同控制[J]. 电力系统自动化, 2022, 46(14): 55-62.
	ZHANG Runfan, BIE Zhaohong. Distributed cluster-level cooperative control of dynamic virtual microgrid cluster for active distribution network[J]. Automation of Electric Power Systems, 2022, 46(14): 55-62.
[6]	魏荣耀, 时光, 庄琨, 等. 基于GPS时间同步的地铁直流牵引供电系统故障测距研究[J]. 船电技术, 2023, 43(8): 85-88.
	WEI Rongyao, SHI Guang, ZHUANG Kun, et al. Research on fault location of subway DC traction power supply system based on GPS time synchronization[J]. Marine Electric & Electronic Engineering, 2023, 43(8): 85-88.
[7]	金雪丰, 李正茂, 胡振. 基于时域微分的地铁直流供电系统故障定位仿真[J]. 船电技术, 2017, 37(4): 67-70.
	JIN Xuefeng, LI Zhengmao, HU Zhen. Simulation of fault location for subway DC power supply system based on time domain differential[J]. Marine Electric & Electronic Engineering, 2017, 37(4): 67-70.
[8]	裴卫卫. 地铁牵引供电系统的可靠性研究[D]. 南京: 南京理工大学, 2019.
	PEI Weiwei. Study on reliability of subway traction power supply system[D]. Nanjing: Nanjing University of Science and Technology, 2019.
[9]	周骏麟. 地铁牵引供电系统可靠性在线分析研究[D]. 上海: 上海交通大学, 2012.
	ZHOU Junlin. On-line reliability analysis of subway traction power supply system[D]. Shanghai: Shanghai Jiao Tong University, 2012.
[10]	刘永生, 魏巍, 王旭, 等. 城市轨道交通交流供电系统快速自愈方案研究[J]. 电气化铁道, 2018, 29(5): 61-64.
	LIU Yongsheng, WEI Wei, WANG Xu, et al. Researches of quick self-healing scheme for AC power supply system of urban mass transit[J]. Electric Railway, 2018, 29(5): 61-64.
[11]	仇龙刚. 直流牵引供电系统故障分析与保护方法研究[D]. 成都: 西南交通大学, 2015.
	QIU Longgang. Fault analysis and protection method research of DC traction power supply system[D]. Chengdu: Southwest Jiaotong University, 2015.
[12]	National Energy Technology Laboratory. The modern grid initative[R]. US: Department of Energy, 2008: 26-30.
[13]	LIU C, JUNG J, HEYDT G T, et al. The strategic power infrastructure defense(SPID) system[J]. IEEE Control Systems Magazine, 2000, 20(4): 40-52.
[14]	张瑶, 王傲寒, 张宏. 中国智能电网发展综述[J]. 电力系统保护与控制, 2021, 49(5): 180-187.
	ZHANG Yao, WANG Aohan, ZHANG Hong. Overview of smart grid development in China[J]. Power System Protection and Control, 2021, 49(5): 180-187.
[15]	李天友. 智能配电网自愈功能及其效益评价模型研究[D]. 北京: 华北电力大学, 2012.
	LI Tianyou. Research on self-healing function and benefit evaluation model of intelligent distribution network[D]. Beijing: North China Electric Power University, 2012.
[16]	AMIN M. Toward self-healing energy infrastructure systems[J]. IEEE Computer Applications in Power, 2001, 14(1):20-28.
[17]	SARIDIS G M. 随机系统的自组织控制[M]. 郑应平,译. 北京: 科学出版社, 1984.
[18]	ROHAMA A, MOHAMED AHMED ALI A, HEGAZY R, et al. DC energy hubs for integration of community DERs, EVs, and subway systems[J]. Sustainability, 2022, 14(3): 1558.
[19]	AHMAD R, SINGH J, MOHAMED A A. Distributed energy resource integration with electrical railway systems: NYC case study[J]. Transportation Electrification: Breakthroughs in Electrified Vehicles, Aircraft, Rolling Stock, and Watercraft, 2022: 399-416.
[20]	ZHONG Z M, ZHANG Y X, SHEN H, et al. Optimal planning of distributed photovoltaic generation for the traction power supply system of high-speed railway[J]. Journal of Cleaner Production, 2020, 263: 121394.
[21]	CHENG P, KONG H W, MA J, et al. Overview of resilient traction power supply systems in railways with interconnected microgrid[J]. CSEE Journal of Power and Energy Systems, 2020, 7(5): 1122-1132.
[22]	POPESCU M, BITOLEANU A. A review of the energy efficiency improvement in DC railway systems[J]. Energies, 2019, 12(6): 12061092.
[23]	余昆. 基于分层递阶控制理论的城市电网自愈控制研究[D]. 杭州: 浙江大学, 2011.
	YU Kun. Research on self-healing control of urban power grid based on hierarchical control theory[D]. Hangzhou: Zhejiang University, 2011.
[24]	刘秋华, 董丹丹, 孟珊珊, 等. 智能配电网自愈控制及其关键技术研究[J]. 南京工程学院学报(自然科学版), 2015, 13(3): 31-36.
	LIU Qiuhua, DONG Dandan, MENG Shanshan, et al. Research on self-healing control and its key technology on smart distribution grid[J]. Journal of Nanjing Institute of Technology(Natural Science Edition), 2015, 13(3):31-36.
[25]	郭志忠. 电网自愈控制方案[J]. 电力系统自动化, 2005, 29(10): 85-91.
	GUO Zhizhong. Scheme of self-healing control frame of power grid[J]. Automation of Electric Power Systems, 2005, 29(10): 85-91.
[26]	ZHOU X S, REN T X, MA Y J, et al. An overview of self-healing control in smart distribution grid[C]// Proceedings of the 36th Chinese Control Conference. IEEE, 2017: 10652-10656.
[27]	康嘉斌. 基于多智能体容错的智能配电网自愈控制方法研究[D]. 南昌: 华东交通大学, 2017.
	KANG Jiabin. Research on self-healing control method of intelligent distribution network based on multi-agent fault tolerance[D]. Nanchang: East China Jiaotong University, 2017.
[28]	常诚. 智能配电网自愈技术研究与应用[D]. 北京: 华北电力大学, 2017.
	CHANG Cheng. Research and application of self-healing technology in intelligent distribution network[D]. Beijing: North China Electric Power University, 2017.
[29]	邵彬. 基于Multi-agent system的智能配电网自愈控制研究[D]. 成都: 西南交通大学, 2015.
	SHAO Bin. Research on self-healing control of intelligent distribution network based on Multi-agent system[D]. Chengdu: Southwest Jiaotong University, 2015.
[30]	LIU H M, CHEN X Y, YU K, et al. The control and analysis of self-healing urban power grid[J]. IEEE Transactions on Smart Grid, 2012,3: 1119-1129.
[31]	胡雯. 自愈机制下含微电网的配电网重构方法研究[D]. 武汉: 武汉大学, 2014.
	HU Wen. Research on reconfiguration method of distribution network with microgrid under self-healing mechanism[D]. Wuhan: Wuhan University, 2014.
[32]	肖鸣晖. 分布式电源广泛接入的分散自治型配电网自愈模式研究[D]. 广州: 华南理工大学, 2017.
	XIAO Minghui. Research on self-healing mode of decentralized autonomous distribution network with widely connected distributed power sources[D]. Guangzhou: South China University of Technology, 2017.
[33]	李春风. 基于multi-agent的智能配电网自愈系统研究[D]. 湘潭: 湖南科技大学, 2012.
	LI Chunfeng. Research on self-healing system of intelligent distribution network based on multi-agent[D]. Xiangtan: Hunan University of Science and Technology, 2012.
[34]	董志辉, 林凌雪, 管霖, 等. 基于多代理技术的有源配电网供电恢复策略[J]. 电力自动化设备, 2019, 39(5): 22-29.
	DONG Zhihui, LIN Lingxue, GUAN Lin, et al. Service restoration strategy of active distribution network based on multi-agent technology[J]. Electric Power Automation Equipment, 2019, 39(5): 22-29.
[35]	魏鑫. 考虑分布式电源的微电网自愈控制技术研究[D]. 济南: 山东大学, 2019.
	WEI Xin. Research on self-healing control technology of microgrid considering distributed generation[D]. Jinan: Shandong University, 2019.
[36]	刘新东, 李伟华, 朱勇, 等. 基于多代理技术的分布式电网自愈控制策略研究[J]. 电力系统保护与控制, 2012, 40(17): 116-120.
	LIU Xindong, LI Weihua, ZHU Yong, et al. Research on self-healing control of distributed networks based on multi-agent technology[J]. Power System Protection and Control, 2012, 40(17): 116-120.
[37]	赵向阳. 主动配电网的自愈控制策略研究[D]. 上海: 上海电力学院, 2018.
	ZHAO Xiangyang. Research on self-healing control strategy of active distribution network[D]. Shanghai: Shanghai University of Electric Power, 2018.
[38]	吴悦华. 有源配电网分布式故障自愈方案与系统开发[D]. 济南: 山东大学, 2019.
	WU Yuehua. Distributed fault self-healing scheme and system development of active distribution network[D]. Jinan: Shandong University, 2019.
[39]	周力. 有源配电网自愈技术研究[D]. 南京: 东南大学, 2017.
	ZHOU Li. Research on self-healing technology of active distribution network[D]. Nanjing: Southeast University, 2017.
[40]	AREFIFAR S A, MOHAMED A R I, EL-FOULY T H M. Comprehensive operational planning framework for self-healing control actions in smart distribution grids[J]. IEEE Transactions on Power Systems, 2013, 28(4): 4192-4200.
[41]	谢忠志, 李曙生. 基于分层多代理的配电网故障自愈恢复系统[J]. 自动化与仪器仪表, 2022(5): 146-150.
	XIE Zhongzhi, LI Shusheng. Self-recovery system based on layered multiple agent[J]. Automation & Instrumentation, 2022(5): 146-150.
[42]	林成, 钟文强, 朱鹏, 等. 基于分层多代理的配电网故障自愈复电系统[J]. 自动化与仪器仪表, 2022(7): 193-197.
	LIN Cheng, ZHONG Wenqiang, ZHU Peng, et al. Self-recovery system based on layered multi-agent[J]. Automation & Instrumentation, 2022(7): 193-197.
[43]	POUYA J, ABHINAV S, FERDINANADA P, et al. Implementation of an Agent-based distributed FLISR algorithm using IEC 61850 in active distribution grids[C]// 4th International Conference on Renewable Energy Research and Applications. IEEE, 2015: 606-611.
[44]	ABDULFETAH A S, MOHAMMED W. Multiagent systems application for the smart grid protection[J]. Renewable and Sustainable Energy Reviews, 2021,149:111352
[45]	ERIKSSON M, ARMENDARIZ M, VASILENKO O O, et al. Multiagent-based distribution automation solution for self-healing grids[J]. IEEE Transactions on Industrial Electronics, 2015, 62(4): 2620-2628.
[46]	朱智勇. 基于大数据和多代理的含IBDGs主动配电网故障自愈系统研究[D]. 湘潭: 湘潭大学, 2017.
	ZHU Zhiyong. Research on fault self-healing system of active distribution network with IBDGs based on big data and multi-agent[D]. Xiangtan: Xiangtan University, 2017.
[47]	刘丹丹. 基于多代理技术的主动配电网故障自愈系统研究[D]. 湘潭: 湘潭大学, 2015.
	LIU Dandan. Research on fault self-healing system of active distribution network based on multi-agent technology[D]. Xiangtan: Xiangtan University, 2015.
[48]	LING W S, LIU D, YANG D X, et al. The situation and trends of feeder automation in China[J]. Renewable and Sustainable Energy Reviews, 2015: 1138-1147.
[49]	KOCH-CIOBOTARU C, MONADI M, LUNA A, et al. Distributed FLISR algorithm for smart grid self-reconfiguration based on IEC 61850[C]// 3rd International Conference on Renewable Energy Research and Applications. IEEE, 2014: 418-423.
[50]	ZHOU J, WANG L, LIU M X, et al. Research on quick distributed feeder automation for fast fault isolation/self-healing in distribution network[C]// 2019 IEEE PES Innovative Smart Grid Technologies Asia. IEEE, 2019: 202-206.
[51]	陈锦龙, 杜江, 郭萌, 等. 基于智能分布式馈线自动化的配电网故障快速自愈方法[J]. 机械与电子, 2021, 39(12): 3-7.
	CHEN Jinlong, DU Jiang, GUO Meng, et al. Fast fault self-healing of distribution network based on intelligent distributed feeder automation[J]. Machinery & Electronics, 2021, 39(12): 3-7.
[52]	王潘潘. 京张高铁智能牵引供电系统自愈重构方案研究[J]. 电气化铁道, 2020, 31(S2): 126-131.
	WANG Panpan. Research on self-healing reconstruction scheme of intelligent traction power supply system for Beijing — Zhangjiakou high-speed railway[J]. Electric Railway, 2020, 31(S2): 126-131.
[53]	井友刚. 电气化铁路广域保护系统自愈重构功能研究与应用[J]. 电气化铁道, 2020, 31(3): 12-15, 32.
	JING Yougang. Study on and application of self-healing reconfiguration function of wide area protection system for electrified railway[J]. Electric Railway, 2020, 31(3): 12-15, 32.
[54]	刘长利. 高速铁路智能牵引供电系统的快速自愈重构技术研究[J]. 铁道标准设计, 2020, 64(4): 162-167.
	LIU Changli. Study on speediness self-healing refactoring technology of high-speed railway intelligent traction power supply system[J]. Railway Standard Design, 2020, 64(4): 162-167.
[55]	余龙. 城市轨道交通中压环网继电保护方案的优化设计[D]. 广州: 华南理工大学, 2010.
	YU Long. Optimal design of relay protection scheme for medium voltage ring network in urban rail transit[D]. Guangzhou: South China University of Technology, 2010.
[56]	陶维青, 杨刚, 丁明, 等. 改进模因算法在含DG配电网故障定位中的应用[J]. 电子测量与仪器学报, 2016, 30(2): 265-273.
	TAO Weiqing, YANG Gang, DING Ming, et al. Application of improved memetic algorithm for fault location in distribution network with DG[J]. Journal of Electronic Measurement and Instrumentation, 2016, 30(2): 265-273.
[57]	杨莉, 周年荣, 李川, 等. 含DG配电网故障定位的免疫二进制萤火虫算法[J]. 化工自动化及仪表, 2021, 48(3): 253-258.
	YANG Li, ZHOU Nianrong, LI Chuan, et al. IBFA for fault location of DG-included power distribution network[J]. Control and Instruments in Chemical Industry, 2021, 48(3): 253-258.
[58]	刘畅宇, 王小君, 尚博阳, 等. 基于渐进式认知发现的新型配电网故障定位方法[J]. 高电压技术, 2024, 50(3): 1156-1164.
	LIU Changyu, WANG Xiaojun, SHANG Boyang, et al. Fault location method based on stepwise cognition-discovery in new distribution network[J]. High Voltage Engineering, 2024, 50(3): 1156-1164.
[59]	郭上华, 王钢. 融合FTU和配变告警信息的有源配网故障定位方法[J]. 电力系统保护与控制, 2022, 50(22): 92-99.
	GUO Shanghua, WANG Gang. An active distribution network fault location method based on FTU and transformer alarm information[J]. Power System Protection and Control, 2022, 50(22): 92-99.
[60]	MONADI M, FARZIN H, RODRIGUEZ P. A distributed self-healing method for active distribution systems[C]// 8th International Conference on Renewable Energy Research and Applications. IEEE: 2019: 483-488.
[61]	李佳玮, 王小君, 和敬涵, 等. 基于图注意力网络的配电网故障定位方法[J]. 电网技术, 2021, 45(6): 2113-2121.
	LI Jiawei, WANG Xiaojun, HE Jinghan, et al. Distribution network fault location based on graph attention network[J]. Power System Technology, 2021, 45(6): 2113-2121.
[62]	孙桂花, 王敬华, 张璇, 等. 分布式智能的小电流接地故障定位方法[J]. 电力系统保护与控制, 2017, 45(16): 72-78.
	SUN Guihua, WANG Jinghua, ZHANG Xuan, et al. Small current grounding fault location method of distributed intelligence[J]. Power System Protection and Control, 2017, 45(16): 72-78.
[63]	李群湛, 王帅, 易东, 等. 电气化铁路贯通同相供电AT牵引网故障辨识与自愈技术研究[J]. 铁道学报, 2022, 44(7): 46-54.
	LI Qunzhan, WANG Shuai, YI Dong, et al. Research on fault identification and self-healing technology of AT traction network with co-phase power supply for electrified railway[J]. Journal of the China Railway Society, 2022, 44(7): 46-54.
[64]	JAMBORSALAMATI P, SADU A, PONCI F, et al. Design, implementation and real-time testing of an IEC 61850 based FLISR algorithm for smart distribution grids[C]// 2015 IEEE International Workshop on Applied Measurements for Power Systems. IEEE: 2015,7312748.
[65]	JAMBORSALAMATI P, SADU A, PONCI F, et al. Improvement of supply restoration in multi-spare-feeder active distribution grids using IEC 61850[C]//2017 IEEE Innovative Smart Grid Technologies-Asia(ISGT-Asia). IEEE, 2017:8378326
[66]	彭晔, 王帅, 王克文. 重载铁路贯通同相牵引网故障自愈研究[J]. 机车电传动, 2022(6): 109-115.
	PENG Ye, WANG Shuai, WANG Kewen. A study on fault self-recovery of interconnected co-phase traction network for heavy haul railway[J]. Electric Drive for Locomotives, 2022(6): 109-115.
[67]	郑龙全, 韩卫卫, 辛晓东. 基于对等通信网络的智能分布式自愈控制的研究[J]. 电工技术, 2022(15): 230-234, 242.
	ZHENG Longquan, HAN Weiwei, XIN Xiaodong. Research on smart distributed self-healing control based on the peer-to-peer communication network[J]. Electric Engineering, 2022(15): 230-234, 242.
[68]	曹云东, 刘锐, 侯春光. 基于GOOSE的智能分布式FA故障自愈研究[J]. 电器与能效管理技术, 2020(1): 31-35.
	CAO Yundong, LIU Rui, HOU Chunguang. Research on intelligent distributed FA fault self-healing based on GOOSE[J]. Electrical & Energy Management Technology, 2020(1): 31-35.
[69]	王宗晖, 陈羽, 徐丙垠, 等. 基于逻辑节点的分布式馈线自动化拓扑识别[J]. 电力系统自动化, 2020, 44(12): 124-130.
	WANG Zonghui, CHEN Yu, XU Bingyin, et al. Logical node based topology identification of distributed feeder automation[J]. Automation of Electric Power Systems, 2020, 44(12): 124-130.
[70]	吴海波, 王牣, 韩正庆. 牵引供电系统越区供电方式自愈重构方案研究[J]. 电气化铁道, 2020, 31(5): 1-4.
	WU Haibo, WANG Ren, HAN Zhengqing. Research on self-healing reconstruction scheme for ver-zone power supply mode in traction power supply system[J]. Electric Railway, 2020, 31(5): 1-4.
[71]	卓梦飞. 基于继电保护与配电自动化的配电网故障自愈技术[D]. 淄博: 山东理工大学, 2019.
	ZHUO Mengfei. Fault self-healing technology of distribution network based on relay protection and distribution automation[D]. Zibo: Shandong University of Technology, 2019.
[72]	古展基. 配电网区域保护与自愈控制系统建模及其通信网络性能分析[D]. 广州: 华南理工大学, 2020.
	GU Zhanji. Modeling of regional protection and self-healing control system of distribution network and performance analysis of its communication network[D]. Guangzhou: South China University of Technology, 2020.
[73]	于琦. 基于移动多代理的配电网故障恢复研究[D]. 秦皇岛: 燕山大学, 2014.
	YU Qi. Research on fault recovery of distribution network based on mobile multi-agent[D]. Qinhuangdao: Yanshan University, 2014.
[74]	王政彤, 吴光龙, 眭浩淼, 等. 基于遗传算法的牵引网智能重构自愈技术研究[J]. 电气化铁道, 2022, 33(2): 42-46.
	WANG Zhengtong, WU Guanglong, SUI Haomiao, et al. Study on intelligent reconfiguration self-healing technology for traction network based on genetic algorithm[J]. Electric Railway, 2022, 33(2): 42-46.
[75]	ARTEM K, MARTIN W, ERIC G. Application of graph theory for automatic restoration distribution networks[C]. Power and Energy Student Summit, 2022: 64-68.
[76]	赵立儒. 基于智能算法的主动配电网自愈及调度优化研究[D]. 秦皇岛: 燕山大学, 2015.
	ZHAO Liru. Research on self-healing and scheduling optimization of active distribution network based on intelligent algorithm[D]. Qinhuangdao: Yanshan University, 2015.
[77]	郑悦. 枢纽牵引供电系统自愈重构方案研究[D]. 成都: 西南交通大学, 2022.
	ZHENG Yue. Research on self-healing reconstruction scheme of traction power supply system in hub[D]. Chengdu: Southwest Jiaotong University, 2022.
[78]	吴静子, 魏登峰. 基于改进粒子群算法的配电网故障自愈算法[J]. 电力学报, 2014, 29(3): 206-210, 222.
	WU Jingzi, WEI Dengfeng. Distribution network fault self-healing algorithm based on improved PSO[J]. Journal of Electric Power, 2014, 29(3): 206-210, 222.
[79]	欧阳卫年, 谭振鹏, 李响. 基于粒子群算法的智能配电网故障自愈控制[J]. 信息技术, 2020, 44(12): 134-138.
	OUYANG Weinian, TAN Zhenpeng, LI Xiang. Fault self healing control of intelligent distribution network based on particle swarm optimization[J]. Information Technology, 2020, 44(12): 134-138.
[80]	吴毅冰, 汪旭东, 吴宏普. 基于二进制粒子群-蚁群算法的配电网自愈控制研究[J]. 通信电源技术, 2017, 34(5): 67-68, 70.
	WU Yibing, WANG Xudong, WU Hongpu. Research on self-recovery control of distribution network based on binary particle swarm ant colony algorithm[J]. Telecom Power Technology, 2017, 34(5): 67-68, 70.
[81]	邱炜. 基于混合蚁群算法的配电网故障区段定位研究[D]. 淄博: 山东理工大学, 2018.
	QIU Wei. Research on fault location of distribution network based on hybrid ant colony algorithm[D]. Zibo: Shandong University of Technology, 2018.
[82]	XU Q, GUO H B, FENG X G, et al. Research on self-healing strategy of smart distribution grid based on improved ant colony algorithm[C]// Proceedings of the 28th Chinese Control and Decision Conference. 2016: 390-395.
[83]	陈真清. 基于人工鱼群算法的城市配电网自愈研究[D]. 天津: 天津城建大学, 2014.
	CHEN Zhenqing. Research on self-healing of urban distribution network based on artificial fish swarm algorithm[D]. Tianjin: Tianjin Chengjian University, 2014.
[84]	党建, 闫运江, 贾嵘, 等. 基于图计算的配电网最大供电能力评估研究[J]. 电网技术, 2022, 46(3): 1039-1049.
	DANG Jian, YAN Yunjiang, JIA Rong, et al. Total supply capability evaluation of distribution network based on graph computation[J]. Power System Technology, 2022, 46(3): 1039-1049.
[85]	郑涛, 潘玉美, 郭昆亚, 等. 基于免疫算法的配电网故障定位方法研究[J]. 电力系统保护与控制, 2014, 42(1): 77-83.
	ZHENG Tao, PAN Yumei, GUO Kunya, et al. Fault location of distribution network based on immune algorithm[J]. Power System Protection and Control, 2014, 42(1): 77-83.
[86]	刘凯, 李文佩, 程潜善, 等. 基于启发式规则和人工免疫的智能配电网自愈[J]. 武汉大学学报(工学版), 2016, 49(2): 218-222.
	LIU Kai, LI Wenpei, CHENG Qianshan, et al. Self-healing of smart distribution network based on heuristic rules and artificial immune[J]. Engineering Journal of Wuhan University, 2016, 49(2): 218-222.
[87]	刘茜. 基于免疫算法和模糊评价的配电网自愈策略研究[D]. 北京: 华北电力大学, 2012.
	LIU Qian. Research on self-healing strategy of distribution network based on immune algorithm and fuzzy evaluation[D]. Beijing: North China Electric Power University, 2012.
[88]	高兴芬, 陈静, 冯辉辉, 等. 基于樽海鞘算法的智能电网故障自愈控制研究[J]. 信息技术, 2022, 46(7): 171-175, 181.
	GAO Xingfen, CHEN Jing, FENG Huihui, et al. Fault self-healing control of intelligent power networks based on salp swarm algorithm[J]. Information Technology, 2022, 46(7): 171-175, 181.
[89]	MARIN-QUINTERO J, OROZCO-HENAO C, MORA-FLOREZ J. Data-driven topology detector for self-healing strategies in Active Distribution Networks[J]. Energy Reports, 2023, 9(S3): 377-385.
[90]	杨华, 李喜旺, 司志坚, 等. 基于图神经网络的配电网故障预测[J]. 计算机系统应用, 2020, 29(9): 131-135.
	YANG Hua, LI Xiwang, SI Zhijian, et al. Accident prediction of power distribution network based on graph neural network[J]. Computer Systems & Applications, 2020, 29(9): 131-135.
[91]	BOYACI O, UMUNNAKWE A, SAHU A, et al. Graph neural networks based detection of stealth false data injection attacks in smart grids[J]. IEEE Systems Journal, 2021, 16(2): 2946-2957.
[92]	张沛, 陈玉鑫, 王光华, 等. 基于图强化学习的配电网故障恢复决策[J]. 电力系统自动化, 2024, 48(2): 151-158.
	ZHANG Pei, CHEN Yuxin, WANG Guanghua, et al. Fault recovery decision of distribution network based on graph reinforcement learning[J]. Automation of Electric Power Systems, 2024, 48(2): 151-158.
[93]	王力, 倪俊, 吕静, 等. 基于混合算法用于配电网多重故障的自愈研究[J]. 湖北民族学院学报(自然科学版), 2018, 36(3): 356-360.
	WANG Li, NI Jun, LYU Jing, et al. Research on self-healing of multiple faults of distribution network based on a hybrid algorithm[J]. Journal of Hubei University for Nationalities (Natural Science Edition), 2018, 36(3): 356-360.
[94]	范鹏逸, 余涛, 王梓耀, 等. 显式集成可靠性表达式的柔性配电网多类型开关优化配置[J/OL]. 中国电机工程学报,1-11(2024-06-12)[2024-10-01]. http://kns.cnki.net/kcms/detail/11.2107.TM.20240612.1349.010.html.
	FAN Pengyi, YU Tao, WANG Xinyao, et al. Optimal deployment of multi-type switches in flexible distribution network explicitly integrating reliability expressions[J/OL]. Proceedings of the CSEE,1-11(2024-06-12)[2024-10-01]. http://kns.cnki.net/kcms/detail/11.2107.TM.20240612.1349.010.html.
[95]	姚建国, 余涛, 杨胜春, 等. 提升电网调度中人工智能可用性的混合增强智能知识演化技术[J]. 电力系统自动化, 2022, 46(20): 1-12.
	YAO Jianguo, YU Tao, YANG Shengchun, et al. Knowledge evolution technology based on hybrid-augmented intelligence for improving practicability of artificial intelligence in power grid dispatch[J]. Automation of Electric Power Systems, 2022, 46(20): 1-12.
[96]	李鹏, 黄文琦, 梁凌宇, 等. 人机混合增强决策智能在新型电力系统调控中的应用与展望[J]. 中国电机工程学报, 2024, 44(16): 6347-6367.
	LI Peng, HUANG Wenqi, LIANG Lingyu, et al. Application and prospect of hybrid human-machine decision intelligence in the dispatch and control of next-era power systems[J]. Proceedings of the CSEE, 2024, 44(16): 6347-6367.
[97]	李鹏, 余涛, 李立浧, 等. 电力人工智能的演变与展望: 从专业智能走向通用智能[J]. 电力系统自动化, 2024, 48(16): 1-17.
	LI Peng, YU Tao, LI Licheng, et al. Retrospect and prospect of artificial intelligence for electric power system: From domain intelligence to general intelligence[J]. Automation of Electric Power Systems, 2024, 48(16): 1-17.
[98]	BRENNA M, FOIADELLI F, KALEYBAR H J. The evolution of railway power supply systems toward smart microgrids: The concept of the energy hub and integration of distributed energy resources[J]. IEEE Electrification Magazine, 2020, 8(1): 12-23.
[99]	LIU H J, TANG T, LYU J D, et al. A dual-objective substation energy consumption optimization problem in subway systems[J]. Energies, 2019, 12(10): 1876.
[100]	DANG J T, ZOU C. Application research of BIM technology in subway power supply and distribution construction[C]// IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2020, 455(1): 012148.
[101]	ROUSTAEI M, NIKNAM T, AGHAEI J, et al. Enhancing smart city operation management: Integrating energy systems with a subway synergism hub[J]. Sustainable Cities and Society, 2024, 107: 105446.
[102]	LIU S W, LU C, HE G N. Distributed electric bicycle batteries for subway station energy management as a virtual power plant[J]. Applied Energy, 2024, 370: 123094.
[103]	程乐峰, 余涛, 张孝顺, 等. 机器学习在能源与电力系统领域的应用和展望[J]. 电力系统自动化, 2019, 43(1): 15-31.
	CHENG Lefeng, YU Tao, ZHANG Xiaoshun, et al. Machine learning for energy and electric power systems: state of the art and prospects[J]. Automation of Electric Power Systems, 2019, 43(1): 15-31.
[104]	李明扬, 窦梦园. 基于强化学习的含电动汽车虚拟电厂优化调度[J]. 综合智慧能源, 2024, 46(6): 27-34. doi: 10.3969/j.issn.2097-0706.2024.06.004
	LI Mingyang, DOU Mengyuan. Optimal scheduling of virtual power plants integrating electric vehicles based on reinforcement learning[J]. Integrated Intelligent Energy, 2024, 46(6): 27-34. doi: 10.3969/j.issn.2097-0706.2024.06.004
[105]	李云, 周世杰, 胡哲千, 等. 基于NSGA-Ⅱ-WPA的综合能源系统多目标优化调度[J]. 综合智慧能源, 2024, 46(4): 1-9. doi: 10.3969/j.issn.2097-0706.2024.04.001
	LI Yun, ZHOU Shijie, HU Zheqian, et al. Optimal scheduling of integrated energy systems based on NSGA-Ⅱ-WPA[J]. Integrated Intelligent Energy, 2024, 46(4): 1-9. doi: 10.3969/j.issn.2097-0706.2024.04.001
[106]	余涛, 程乐峰, 张孝顺. 基于信息-物理-社会系统融合和群体机器学习的弱中心化微元网:理论研究与关键科学问题分析[J]. 中国科学: 技术科学, 2019, 49(12): 1541-1569.
	YU Tao, CHENG Lefeng, ZHANG Xiaoshun. The weakly-centralized Web-of-Cells based on cyber-physical-social systems integration and group machine learning: Theoretical investigations and key scientific issues analysis[J]. Scientia Sinica (Technologica), 2019, 49(12): 1541-1569.
[107]	李明扬, 董哲. 含分布式新能源和需求响应负荷的虚拟电厂定价机制及优化调度[J]. 综合智慧能源, 2024, 46(10): 12-17. doi: 10.3969/j.issn.2097-0706.2024.10.002
	LI Mingyang, DONG Zhe. Pricing mechanism and optimal scheduling of virtual power plants containing distributed renewable energy and demand response loads[J]. Integrated Intelligent Energy, 2024, 46(10): 12-17. doi: 10.3969/j.issn.2097-0706.2024.10.002
[108]	陈聪. 柔性直流配电系统保护及定位技术研究[D]. 北京: 华北电力大学, 2022.
	CHEN Cong. Research on protection and location technology of flexible DC distribution system[D]. Beijing: North China Electric Power University, 2022.
[109]	冯骥, 杨国华, 史磊, 等. 基于并行融合深度残差收缩网络的有源配电网故障诊断[J]. 综合智慧能源, 2024, 46(6): 8-15. doi: 10.3969/j.issn.2097-0706.2024.06.002
	FENG Ji, YANG Guohua, SHI Lei, et al. Research on fault diagnosis of active distribution network based on parallel fusion deep residual shrinkage network[J]. Integrated Intelligent Energy, 2024, 46(6): 8-15. doi: 10.3969/j.issn.2097-0706.2024.06.002
[110]	刘子奕, 贾科, 姚昆鹏, 等. 基于主动注入的柔性直流配电网故障定位[J]. 电力系统保护与控制, 2023, 51(18): 21-30.
	LIU Ziyi, JIA Ke, YAO Kunpeng, et al. An active converter injection-based fault location method for a flexible DC distribution network[J]. Power System Protection and Control, 2023, 51(18): 21-30.

恢复技术	故障
备自投、重合闸	简易的、确定的故障
FA技术	预设场景、确定的故障
保护控制协同	保护动作、驱动对应的恢复