Method of power distribution network fault diagnosis based on improved time fuzzy petri net

被引：0
作者：
Liu X.-R. [1 ]
Gao Y.-W. [1 ]
Wang Z.-L. [1 ]
机构：
[1] School of Information Science & Engineering, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2016年 / 37卷 / 11期
关键词：
Alarms information; Distribution network; Fault diagnosis; Petri net; Time fuzzy;
D O I：
10.3969/j.issn.1005-3026.2016.11.002
中图分类号：
学科分类号：
摘要：
In distribution network, the equipments in different regions have different reliabilities, and the automation of fault diagnosis is at low level. In order to make the fault diagnosis quick and accurate, a new method of distribution network fault diagnosis was presented based on improved time fuzzy Petri net. Firstly, the information of breakers with the switch and electrical information were corrected, and the areas where power collapse were found out. Then, according to the range that the breaker can protect, the suspicious fault elements were found out, and the time fuzzy Petri net model for suspicious fault elements with time-tag information was built. Finally the diagnosis results were given. The simulation results show that the efficiency and accuracy of fault diagnosis are improved. In addition, the fault process can be shown for dispatchers with good practical application value. © 2016, Editorial Department of Journal of Northeastern University. All right reserved.
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页码：1526 / 1529
页数：3
相关论文
共 10 条
  • [1] Cho H.J., Park J.K., An expert system for fault section diagnosis of power systems using fuzzy relations, IEEE Transactions on Power Systems, 12, 1, pp. 342-348, (1997)
  • [2] Chen W.H., Fault section estimation using fuzzy matrix-based reasoning methods, IEEE Transactions on Power Delivery, 26, 1, pp. 205-213, (2011)
  • [3] Decanini J.G.M.S., Tonelli-Neto M.S., Minussi C.R., Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory, IET Generation, Transmission & Distribution, 6, 11, pp. 1112-1120, (2012)
  • [4] Wu N., Chu F., Chu C., Et al., Schedulability analysis of short-term scheduling for crude oil operations in refinery with oil residency time and charging-tank-switch-overlap constraints, IEEE Transactions on Automation Science & Engineering, 8, 1, pp. 190-204, (2011)
  • [5] Chen C.S., Ke Y.L., Wu J.S., Et al., Application of Petri nets to solve distribution system contingency by considering customer load patterns, IEEE Transactions on Power Systems, 17, 2, pp. 417-423, (2002)
  • [6] Calderaro V., Hadjicostis C.N., Piccolo A., Failure identification in smart grids based on Petri net modeling, IEEE Transactions on Industrial Electronics, 58, 10, pp. 4613-4623, (2011)
  • [7] Yang J.-W., He Z.-Y., Zang T.-L., Power system fault-diagnosis method based on directional weighted fuzzy Petri nets, Proceedings of the CSEE, 30, 34, pp. 42-49, (2010)
  • [8] Chen S.M., Weighted fuzzy reasoning using weighted fuzzy Petri nets, IEEE Transactions on Knowledge and Data Engineering, 14, 2, pp. 386-397, (2002)
  • [9] Xu L., Kezunovic M., Implementing fuzzy reasoning Petri nets for fault section estimation, IEEE Transactions on Power Delivery, 23, 2, pp. 676-685, (2008)
  • [10] Bi T.-S., Yang C.-F., Huang S.-F., Et al., Improved Petri net models based fault diagnosis approach of power networks, Power System Technology, 29, 21, pp. 52-56, (2005)
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