Abstract
In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global single-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.
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References
Cardozo, E., Talukdar, S.N., 1988. A distributed expert system of fault diagnosis. IEEE Trans. on Power Syst., 3(2):641–646. [doi:10.1109/59.192917]
Chantler, M., Pogliano, P., Aldea, A., Tornielli, G., Wyatt, T., Jolley, A., 2000. Use of fault-recorder data for diagnosing timing and other related faults in electricity transmission networks. IEEE Trans. on Power Syst., 15(4):1388–1393. [doi:10.1109/59.898117]
Chin, H.C., 2003. Fault section diagnosis of power system using fuzzy logic. IEEE Trans. on Power Syst., 18(1):245–250. [doi:10.1109/TPWRS.2002.807095]
Chuang, C.Y., Ke, Y., Chen, Y.L., 2006. Rule-expert knowledge-based Petri net approach for distribution system temperature adaptive feeder reconfiguration. IEEE Trans. on Power Syst., 21(3):1362–1370. [doi:10.1109/TPWRS.2006.876681]
Dysko, A., McDonald, J.R., Burt, G.M., Goody, J., Gwyn, B., 1999. Integrated Modelling Environment: A Platform for Dynamic Protection Modelling and Advanced Functionality. Proc. IEEE Transmission Distribution Conf., 1:406–411. [doi:10.1109/TDC.1999.755386]
Hu, Z.H., Cai, Y.Z., Li, Y.G., 2005. Data fusion for fault diagnosis using multi-class support vector machines. J. Zhejiang Univ. Sci., 6A(10):1030–1039. [doi:10.1631/jzus.2005.A1030]
Huang, S.J., 2002. Application of immune-based optimization method for fault-section estimation in a distribution system. IEEE Trans. on Power Del., 17(3):779–784. [doi:10.1109/TPWRD.2002.1022803]
Huang, Y.C., 2002. Fault section estimation in power systems using a novel decision support system. IEEE Trans. on Power Syst., 17(2):439–444. [doi:10.1109/TPWRS.2002.1007915]
Liu, L., Logan, K.P., Cartes, D.A., Srivastava, S.K., 2007. Fault detection, diagnostics, and prognostics: software agent solutions. IEEE Trans. on Vehic. Technol., 56(4):1613–1622. [doi:10.1109/TVT.2007.897219]
Narendra, K.G., Sood, V.K., Khorasani, K., 1998. Application of a Radial Basis Function (RBF) neural network for fault diagnosis in a HVDC system. IEEE Trans. on Power Syst., 13(1):177–183. [doi:10.1109/59.651633]
Park, Y.M., Kim, G.W., Sohn, J.M., 1997. A Logic Based Expert System, LBES, for Fault Diagnosis of Power System. IEEE Trans. on Power Syst., 12(1):363–369. [doi:10.1109/59.574960]
Ren, H., Mi, Z.Q., Zhao, H.S., 2005. Power system fault diagnosis by use of encoded Petri net models. Proc. CSEE, 25(20):44–49 (in Chinese).
Sun, Y.M., Jiang, H., Wang, D., 1998. Fault synthetic recognition for an EHV transmission line using a group of neural networks with a time-space property. IEE Proc.-Gener., Transm. Distrib., 145(3):265–270. [doi:10.1049/ip-gtd:19981919]
Wen, F.S., Chang, C.S., 1996. A New Approach to Fault Section Estimation in Power Systems Based on the Set Covering Theory and a Refined Genetic Algorithm. Proc. 12th Power Systems Computation Conf., Dresden, Germany, 1:358–365.
Wen, F.S., Han, Z.X., 1997. Probabilistic approach for fault section estimation in power systems based upon a refined genetic algorithm. IEE Proc.-Gener., Transm. Distrib., 144(2):160–168. [doi:10.1049/ip-gtd:19970802]
Zhu, Y.L., Huo, L.M., Lu, J.L., 2006. Bayesian networks-based approach for power systems fault diagnosis. IEEE Trans. on Power Delivery, 21(2):634–639. [doi:10.1109/TPWRD.2005.858774]
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Project supported by the National Natural Science Foundation of China (No. 50677062), the New Century Excellent Talents in University of China (No. NCET-07-0745) and the Natural Science Foundation of Zhejiang Province, China (No. R107062)
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Liu, Y., Li, Y., Cao, Yj. et al. Forward and backward models for fault diagnosis based on parallel genetic algorithms. J. Zhejiang Univ. Sci. A 9, 1420–1425 (2008). https://doi.org/10.1631/jzus.A0720087
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DOI: https://doi.org/10.1631/jzus.A0720087
Key words
- Forward and backward models
- Fault diagnosis
- Global single-population master-slave genetic algorithms (GPGAs)
- Parallel computation