Fault diagnosis method for substations based on fault enumeration tree to generate fuzzy Petri net
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摘要:
针对目前考虑时序信息的Petri网故障诊断模型复杂、自动建模困难的问题，提出了一种基于故障枚举树生成模糊Petri网的变电站故障诊断方法。为了完备地遍历变电站系统中各故障组合场景，提出了一种基于广度优先搜索的故障枚举树遍历方法，实现了变电站一二次故障组合的快速遍历仿真。基于仿真结果建立变电站模糊时序Petri网故障诊断模型，通过并行推理计算实现变电站故障快速诊断。在CloudPSS云仿真平台建立典型110 kV变电站的一二次联动仿真算例进行测试，结果表明：该方法将保护设备和断路器的动作时限信息计入故障推理过程，考虑在经典Petri 网模型的矩阵运算推理基础上加入动作时限与告警信息的时间戳比对计算，因此在部分告警信息失真的情况下依然有较好的诊断效果；该方法可在保证故障诊断准确性的情况下应用于更加复杂的二次保护系统；采用并行分层矩阵推理算法，提升了诊断模型的推理效率，具有较高实用价值；在推理计算过程中对置信度增加了修正步骤，更加充分地考虑了保护拒动或误动及告警信息误报或漏报对故障诊断的影响，对于复杂故障情况有更高的容错性。
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关键词:
- 变电站 /
- 故障诊断 /
- 继电保护 /
- CloudPSS建模仿真 /
- 模糊Petri网
Abstract:To address the issues of complexity and difficulty of automatic modeling in Petri net fault diagnosis models that incorporate temporal information, a fault diagnosis method for substations based on generating fuzzy Petri nets from fault enumeration trees is proposed. To comprehensively traverse all fault combination scenarios in the substation system, a fault enumeration tree traversal method based on Breadth-First Search (BFS) was developed, enabling the rapid simulation of primary and secondary fault combinations in substations. Based on the simulation results, a fuzzy temporal Petri net fault diagnosis model for substations was established, and parallel inference was employed for fast fault diagnosis. Primary and secondary interconnected simulation for a typical 110 kV substation was tested on the CloudPSS cloud simulation platform. The test results indicated that the proposed method incorporated the action time limit information of protection devices and circuit breakers into the fault inference process. By comparing the timestamps for action time limits and alarm information, alongside matrix operations in the classical Petri net model, the method demonstrated strong diagnostic performance, even in the case of distorted alarm information. This approach could be applied to more complex secondary protection systems while ensuring fault diagnosis accuracy. The parallel hierarchical matrix inference algorithm improved the inference efficiency of the diagnostic model, providing significant practical value. Additionally, the inference process included a confidence correction step, which more thoroughly considered the impact of protection failures, false trips, and misreported or omitted alarm information on fault diagnosis, improving fault tolerance in complex fault scenarios.
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Keywords:
- substation /
- fault diagnosis /
- relay protection /
- CloudPSS modeling and simulation /
- fuzzy Petri net
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图 4 110 kV线路Ⅰ（L1）模糊Petri网故障诊断模型
Figure 4. 110 kV lineⅠ(L1) fuzzy Petri net fault diagnosis model
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图 5 由仿真结果生成的一条Petri网模型分支
Figure 5. A Petri net model branch generated from simulation results
表 1 变电站继电保护配置
Table 1 Substation relay protection configuration
保护位置 保护类型 动作时延/s 备注 110 kV线路 纵联差动 0 距离Ⅱ/Ⅲ段 0.3/1.8 零序Ⅱ/Ⅲ/Ⅳ段 0.3/0.6/1.8 经方向闭锁 110 kV母线 差动保护 0 110 kV备自投 0.3 主变 纵联差动 0 跳各侧 高复压过流 1.5 跳各侧 高零序Ⅰ段1/2时限 1.5/1.8 跳高压分段/高压侧 高零序Ⅱ段1/2时限 2.1/2.4 跳高压分段/各侧 高间隙过流 1.5 跳各侧 高零序过压 1.5 跳各侧 低过流Ⅰ段1/2时限 0.9/1.2 跳低压侧/各侧 低复压过流Ⅱ段
1/2/3时限0.9/1.2/1.5 跳10 kV分段/低压侧/各侧 10 kV母联分段 过流Ⅰ/Ⅱ段 0.6/0.9 10 kV备自投 0.3 10 kV线路 过流Ⅰ/Ⅱ段 0.4/0.9 零序过流Ⅰ/Ⅱ段 1/3 一次重合闸 1 站用变 电流速断 0 过流Ⅰ段 0.9 高零序过流 0.6 低零序过流 2 接地变 电流速断 0 过流Ⅰ段 3.2 零序过流1/2时限 2.3/2.6 跳主变低/10 kV分段 电容器 过流Ⅰ/Ⅱ段 0.1/0.6 零序Ⅰ段 0.6 零序电压保护 0.2 过电压保护 5 低电压保护 0.6 
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表 2 仿真输出的告警日志
Table 2 Alarm log output from simulation
时间/s 告警信息 0.516 1号主变差动保护动作 0.519 1号主变高压侧断路器断开 0.519 1号主变低压侧断路器断开 4.169 10 kV备自投动作 4.170 10 kV分段开关1号断路器闭合
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表 3 断路器和继电保护库所初始化赋值规则
Table 3 Initialization assignment rules for circuit breakers and relay protection libraries
设备 置信度 收到告警信息 未收到告警信息 继电保护 0.95 0.20 断路器 0.90 0.20
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表 4 设备异常告警评价规则
Table 4 Equipment abnormal alarm evaluation rules
设备 告警情况 推理结果 评价 继电保护设备${P_i}$ 有动作告警信息 ${P_i} \in {{{{P}}}}_{{\mathrm{\;B}}3}^{{\mathrm{{\mathrm{\;res}}}}}$ 正常 $ P_i\notin(P_{\mathrm{B}3}^{\mathrm{res}}\cup P_{\mathrm{B}2}^{\mathrm{res}}) $ 误动 $ P_i\in P_{\mathrm{B}2}^{\mathrm{res}} $ 误报 无动作告警信息 $ P_i\notin(P_{\mathrm{B}3}^{\mathrm{res}}\cup P_{\mathrm{B}2}^{\mathrm{res}}) $ 正常 $ P_i\in P_{\mathrm{B}2}^{\mathrm{res}} $ 拒动 $ P_i\in P_{\mathrm{B}3}^{\mathrm{res}} $ 漏报 断路器${P_k}$ 有动作告警信息 $ P_k\in P_{\mathrm{B}4}^{\mathrm{res}} $ 正常 $ P_k\notin P_{\mathrm{B}4}^{\mathrm{res}} $ 误动或误报 无动作告警信息 $ P_k\notin P_{\mathrm{B}4}^{\mathrm{res}} $ 正常 $ P_k\in P_{\mathrm{B}4}^{\mathrm{res}} $ 拒动或漏报
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表 5 批量仿真数据集
Table 5 Batch simulation dataset
故障点 K 故障点 K 110 kV线路Ⅰ 310 10 kV分段开关2号 1 127 110 kV线路Ⅱ 310 10 kV出线Ⅰ 32 110 kV母线Ⅰ 168 10 kV出线Ⅱ 32 110 kV母线Ⅱ 190 10 kV出线Ⅲ 32 110 kV母联分段开关 50 10 kV出线Ⅳ 32 1号主变高压侧 232 1号站用变高压侧 61 1号主变低压侧 130 1号站用变低压侧 12 2号主变高压侧 320 2号站用变高压侧 85 2号主变低压侧 756 2号站用变低压侧 12 3号主变高压侧 184 1号接地变 57 3号主变低压侧 330 2号接地变 99 10 kV母线Ⅰ 126 3号接地变 57 10 kV母线ⅡA 242 电容器组Ⅰ 28 10 kV母线ⅡB 219 电容器组Ⅱ 28 10 kV母线Ⅲ 126 电容器组Ⅲ 28 10 kV分段开关1号 2 010 电容器组Ⅳ 28
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表 6 仿真测试结果
Table 6 Simulation test results
算例设置 告警信息 本文方法推理结果 文献[23]方法推理结果 故障位置 设备状态 故障位置 动作评价 故障位置 动作评价 1号主变
低压侧正常 3.606 s，1号接地变零序过流保护动作
3.610 s，1号主变低压侧断路器断开
4.659 s，10 kV备自投动作
4.660 s，10 kV分段1号开关断路器闭合1号主变
低压侧正常 1号主变
低压侧正常 3号主变
低压侧3号主变
差动保护拒动2.807 s,110 kV线路Ⅱ距离Ⅲ段动作
2.814 s，110 kV线路Ⅱ送端断路器断开
5.383 s，110 kV备自投判断线路Ⅱ失压
5.384 s，110 kV线路Ⅱ受端断路器断开
5.584 s，110 kV备自投动作
5.585 s，110 kV母联分段开关闭合
7.396 s，110 kV线路Ⅰ距离Ⅲ段动作
7.404 s，110 kV线路Ⅰ送端断路器断开3号主变
低压侧3号主变
差动保护拒动3号主变
低压侧3号主变
差动保护拒动110 kV
母线Ⅱ母线差动保护拒动
110 kV备自投漏报1.306 s，110 kV线路Ⅱ距离Ⅱ段动作
1.307 s，110 kV线路Ⅱ送端断路器断开
3.560 s，110 kV备自投判断线路Ⅱ失压
3.562 s，110 kV线路Ⅱ受端断路器断开
3.763 s，110 kV母联分段开关闭合
4.066 s，110 kV线路Ⅰ距离Ⅱ段动作
4.067 s，110 kV线路Ⅰ送端断路器断开110 kV
母线Ⅱ母线差动保护拒动
110 kV备自投漏报110 kV
线路Ⅰ110 kV线路Ⅰ
纵联差动保护拒动110 kV
线路Ⅱ110 kV线路Ⅱ
纵联差动保护拒动
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[1] 周子冠,白晓民,李再华,等. 采用知识网格技术的智能输电网故障诊断方法[J]. 中国电机工程学报,2010,30(4):8-15. ZHOU Ziguan,BAI Xiaomin,LI Zaihua,et al. Novel fault diagnosis approach of smart transmission grid based on knowledge grid technology[J]. Proceedings of the CSEE,2010,30(4):8-15.
[2] CARDOSO G,ROLIM J G,ZURN H H. Identifying the primary fault section after contingencies in bulk power systems[J]. IEEE Transactions on Power Delivery,23(3):1335-1342.
[3] 熊国江,石东源,朱林,等. 基于径向基函数神经网络的电网模糊元胞故障诊断[J]. 电力系统自动化,2014,38(5):59-65. DOI: 10.7500/AEPS20130603005 XIONG Guojiang,SHI Dongyuan,ZHU Lin,et al. Fuzzy cellular fault diagnosis of power grids based on radial basis function neural network[J]. Automation of Electric Power Systems,2014,38(5):59-65. DOI: 10.7500/AEPS20130603005
[4] FLORES NOVELO A,QUILES CUCARELLA E,GARCIA MORENO E,et al. Fault diagnosis of electric transmission lines using modular neural networks[J]. IEEE Latin America Transactions,14(8):3663-3668.
[5] 孙明蔚,童晓阳,刘新宇,等. 运用时序贝叶斯知识库的电网故障诊断方法[J]. 电网技术,2014,38(3):715-722. SUN Mingwei,TONG Xiaoyang,LIU Xinyu,et al. A power system fault diagnosis method using temporal Bayesian knowledge bases[J]. Power System Technology,2014,38(3):715-722.
[6] 罗孝辉,童晓阳. 计及可信度的变结构贝叶斯网络电网故障诊断[J]. 电网技术,2015,39(9):2658-2664. LUO Xiaohui,TONG Xiaoyang. Structure-variable Bayesian network for power system fault diagnosis considering credibility[J]. Power System Technology,2015,39(9):2658-2664.
[7] 栗然,张烈勇,顾雪平,等. 采用粗糙集联合规则挖掘算法的分布式电网故障诊断[J]. 中国电机工程学报,2010,30(4):28-34. LI Ran,ZHANG Lieyong,GU Xueping,et al. Distributed fault diagnosis of power networks applying the united rules mining algorithm based on rough set theory[J]. Proceedings of the CSEE,2010,30(4):28-34.
[8] SUN Qiuye,WANG Chunling,WANG Zhiliang,et al. A fault diagnosis method of smart grid based on rough sets combined with genetic algorithm and tabu search[J]. Neural Computing and Applications,2013,23(7):2023-2029.
[9] GUO Wenxin,WEN Fushuan,LEDWICH G,et al. An analytic model for fault diagnosis in power systems considering malfunctions of protective relays and circuit breakers[J]. IEEE Transactions on Power Delivery,25(3):1393-1401.
[10] 刘道兵,顾雪平,李海鹏. 电网故障诊断的一种完全解析模型[J]. 中国电机工程学报,2011,31(34):85-92,10. LIU Daobing,GU Xueping,LI Haipeng. A complete analytic model for fault diagnosis of power systems[J]. Proceedings of the CSEE,2011,31(34):85-92,10.
[11] 高正中,许焕奇,雷倩,等. 基于Petri网和双层判据的配电网故障定位方法[J]. 工矿自动化,2016,42(8):37-42. GAO Zhengzhong,XU Huanqi,LEI Qian,et al. Fault location method for distribution network based on Petri net and double criterions[J]. Industry and Mine Automation,2016,42(8):37-42.
[12] 王守鹏,赵冬梅. 电网故障诊断的研究综述与前景展望[J]. 电力系统自动化,2017,41(19):164-175. DOI: 10.7500/AEPS20170126001 WANG Shoupeng,ZHAO Dongmei. Research review and prospects for power grid fault diagnosis[J]. Automation of Electric Power Systems,2017,41(19):164-175. DOI: 10.7500/AEPS20170126001
[13] LO K L,NG H S,TRECAT J. Power systems fault diagnosis using Petri nets[J]. IEE Proceedings -Generation,Transmission and Distribution,1997,144(3):231-236. DOI: 10.1049/ip-gtd:19971060
[14] LO K L,NG H S,TRECAT J. Extended Petri net models for fault diagnosis for substation automation[J]. IEE Proceedings-Generation,Transmission and Distribution,1999,146(3):229-234. DOI: 10.1049/ip-gtd:19990071
[15] SUN Jing, QIN Shiyin, SONG Yonghua. Fault diagnosis of electric power systems based on fuzzy Petri nets[J]. IEEE Transactions on Power Systems,2004,19(4):2053-2059. DOI: 10.1109/TPWRS.2004.836256
[16] 孙静,秦世引,宋永华. 模糊PETRI网在电力系统故障诊断中的应用[J]. 中国电机工程学报,2004,24(9):74-79. SUN Jing,QIN Shiyin,SONG Yonghua. Fuzzy PETRI nets and its application in the fault diagnosis of electric power systems[J]. Proceedings of the CSEE,2004,24(9):74-79.
[17] LUO Xu,KEZUNOVIC M. Implementing fuzzy reasoning Petri-nets for fault section estimation[J]. IEEE Transactions on Power Delivery,23(2):676-685.
[18] 毕天姝,杨春发,黄少锋,等. 基于改进Petri网模型的电网故障诊断方法[J]. 电网技术,2005,29(21):56-60. BI Tianshu,YANG Chunfa,HUANG Shaofeng,et al. Improved Petri net models based fault diagnosis approach for power networks[J]. Power System Technology,2005,29(21):56-60.
[19] 杨健维,何正友. 基于时序模糊Petri网的电力系统故障诊断[J]. 电力系统自动化,2011,35(15):46-51. YANG Jianwei,HE Zhengyou. Power system fault diagnosis approach based on time sequence fuzzy Petri net[J]. Automation of Electric Power Systems,2011,35(15):46-51.
[20] 谢红涛,童晓阳. 基于分层模糊Petri网的电网故障综合诊断方法[J]. 电网技术,2012,36(1):246-252. XIE Hongtao,TONG Xiaoyang. A method of synthetical fault diagnosis for power system based on fuzzy hierarchical Petri net[J]. Power System Technology,2012,36(1):246-252.
[21] 童晓阳,谢红涛,孙明蔚. 计及时序信息检查的分层模糊Petri网电网故障诊断模型[J]. 电力系统自动化,2013,37(6):63-68. DOI: 10.7500/AEPS201107206 TONG Xiaoyang,XIE Hongtao,SUN Mingwei. Power system fault diagnosis model based on layered fuzzy Petri net considering temporal constraint checking[J]. Automation of Electric Power Systems,2013,37(6):63-68. DOI: 10.7500/AEPS201107206
[22] 徐彪,尹项根,汪旸,等. 基于模糊时间Petri网的电网故障诊断方法[J]. 电力系统自动化,2018,42(2):70-76. DOI: 10.7500/AEPS20170320004 XU Biao,YIN Xianggen,WANG Yang,et al. Fault diagnosis method of power system based on fuzzy time Petri net[J]. Automation of Electric Power Systems,2018,42(2):70-76. DOI: 10.7500/AEPS20170320004
[23] XU Biao,YIN Xin,YIN Xianggen,et al. Fault diagnosis of power systems based on temporal constrained fuzzy Petri nets[J]. IEEE Access,2019,7:101895-101904. DOI: 10.1109/ACCESS.2019.2930545
[24] 陈颖,高仕林,宋炎侃,等. 面向新型电力系统的高性能电磁暂态云仿真技术[J]. 中国电机工程学报,2022,42(8):2854-2864. CHEN Ying,GAO Shilin,SONG Yankan,et al. High-performance electromagnetic transient simulation for new-type power system based on cloud computing[J]. Proceedings of the CSEE,2022,42(8):2854-2864.
[25] 章耀耀,侯勇,叶海,等. 继电保护数字孪生技术应用展望[J]. 电力系统保护与控制,2023,51(12):178-187. ZHANG Yaoyao,HOU Yong,YE Hai,et al. Application prospect for relay protection digital twin technology[J]. Power System Protection and Control,2023,51(12):178-187.
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