Utilizing analytical hierarchy process and smart techniques in fault detection in electrical power networks

L. Zhao, P. Zhang, J. Huang *

School of Electronic Science and Engineering, Nanjing University, Nanjing, China

Abstract

In this study a smart hybrid technique is presented to detect faults in electrical power networks. The accurate and automatic recognition of faults in electrical power networks has vital importance. The goal of accurate and automatic recognition of faults in electrical power networks is to control and monitor the system that giving security to final users for stable voltage and normal condition. To solve the fault recognition problem in power networks, it has been considered a synergy among optimization, state space and analytical approach by building an algorithm for its solution. In the first step, state space module executes through oscillation signaled using its output, an analysis with relation to the direction of fault in each appurtenance of the power network. The state space can determine the accurate direction of the disturbance. The final task in fault recognition is done by the optimization module and analytical module. In the proposed method, the location and class will be determined. To test the performance of the proposed hybrid method, we used some electrical power networks. The obtained results show that the proposed method has high accuracy in fault recognition of electrical power networks.

Keywords

State space, Fault, Power network, Optimization, Recognition

Digital Object Identifier (DOI)

https://doi.org/10.21833/AEEE.2019.05.004

Article history

Received 5 January 2019, Received in revised form 22 March 2019, Accepted 10 April 2019

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How to cite

Zhao L, Zhang P, and Huang J (2019). Utilizing analytical hierarchy process and smart techniques in fault detection in electrical power networks. Annals of Electrical and Electronic Engineering, 2(5): 19-25

References (33)

  1. Abhishek K, Panda BN, Datta S, and Mahapatra SS (2014). Comparing predictability of genetic programming and ANFIS on drilling performance modeling for GFRP composites. Procedia Materials Science, 6: 544-550. https://doi.org/10.1016/j.mspro.2014.07.069   [Google Scholar]
  2. Abido MA and Bakhashwain JM (2005). Optimal VAR dispatch using a multiobjective evolutionary algorithm. International Journal of Electrical Power and Energy Systems, 27(1): 13-20. https://doi.org/10.1016/j.ijepes.2004.07.006   [Google Scholar]
  3. Akib S, Mohammadhassani M, and Jahangirzadeh A (2014). Application of ANFIS and LR in prediction of scour depth in bridges. Computers and Fluids, 91: 77-86. https://doi.org/10.1016/j.compfluid.2013.12.004   [Google Scholar]
  4. Åström KJ, Hägglund T, and Astrom KJ (2006). Advanced PID control. Vol. 461, ISA-The Instrumentation, Systems, and Automation Society, Research Triangle Park, North Carolina, USA.   [Google Scholar]
  5. Avci E, Hanbay D, and Varol A (2007). An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition. Expert Systems with Applications, 33(3): 582-589. https://doi.org/10.1016/j.eswa.2006.06.001   [Google Scholar]
  6. Banaei MR, Seyed-Shenava SJ, and Farahbakhsh P (2014). Dynamic stability enhancement of power system based on a typical unified power flow controllers using imperialist competitive algorithm. Ain Shams Engineering Journal, 5(3): 691-702. https://doi.org/10.1016/j.asej.2014.01.003   [Google Scholar]
  7. Bhattacharyya B and Gupta VK (2014). Fuzzy based evolutionary algorithm for reactive power optimization with FACTS devices. International Journal of Electrical Power and Energy Systems, 61: 39-47. https://doi.org/10.1016/j.ijepes.2014.03.008   [Google Scholar]
  8. Crowe J, Chen GR, Ferdous R, Greenwood DR, Grimble MJ, Huang HP, and Lee TH (2005). PID control: New identification and design methods. Springer, London, UK.   [Google Scholar]
  9. Dong F, Chowdhury BH, Crow ML, and Acar L (2005). Improving voltage stability by reactive power reserve management. IEEE Transactions on Power Systems, 20(1): 338-345. https://doi.org/10.1109/TPWRS.2004.841241   [Google Scholar]
  10. Esmin AA, Lambert-Torres G, and De Souza AZ (2005). A hybrid particle swarm optimization applied to loss power minimization. IEEE Transactions on Power Systems, 20(2): 859-866. https://doi.org/10.1109/TPWRS.2005.846049   [Google Scholar]
  11. Fang H, Chen L, and Shen Z (2011). Application of an improved PSO algorithm to optimal tuning of PID gains for water turbine governor. Energy Conversion and Management, 52(4): 1763-1770. https://doi.org/10.1016/j.enconman.2010.11.005   [Google Scholar]
  12. Gasperic S and Mihalic R (2015). The impact of serial controllable FACTS devices on voltage stability. International Journal of Electrical Power and Energy Systems, 64: 1040-1048. https://doi.org/10.1016/j.ijepes.2014.08.010   [Google Scholar]
  13. Ghoshal SP (2004). Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control. Electric Power Systems Research, 72(3): 203-212. https://doi.org/10.1016/j.epsr.2004.04.004   [Google Scholar]
  14. Gitizadeh M, Pilehvar MS, and Mardaneh M (2013). A new method for SVC placement considering FSS limit and SVC investment cost. International Journal of Electrical Power and Energy Systems, 53: 900-908. https://doi.org/10.1016/j.ijepes.2013.06.009   [Google Scholar]
  15. Huang JS, Jiang ZH, and Negnevitsky M (2013). Loadability of power systems and optimal SVC placement. International Journal of Electrical Power and Energy Systems, 45(1): 167-174. https://doi.org/10.1016/j.ijepes.2012.08.064   [Google Scholar]
  16. Kano M and Ogawa M (2009). The state of art in advanced process control in Japan. IFAC Proceeding Volumes, 42(11): 10-25. https://doi.org/10.3182/20090712-4-TR-2008.00005   [Google Scholar]
  17. Khorramdel B and Raoofat M (2012). Optimal stochastic reactive power scheduling in a microgrid considering voltage droop scheme of DGs and uncertainty of wind farms. Energy, 45(1): 994-1006. https://doi.org/10.1016/j.energy.2012.05.055   [Google Scholar]
  18. Monticelli A (2012). State estimation in electric power systems: A generalized approach. Springer Science and Business Media, Berlin, Germany.   [Google Scholar]
  19. Moreno R, Moreira R, and Strbac G (2015). A MILP model for optimising multi-service portfolios of distributed energy storage. Applied Energy, 137: 554-566. https://doi.org/10.1016/j.apenergy.2014.08.080   [Google Scholar]
  20. Mousavi OA and Cherkaoui R (2014). Investigation of P–V and V–Q based optimization methods for voltage and reactive power analysis. International Journal of Electrical Power and Energy Systems, 63: 769-778. https://doi.org/10.1016/j.ijepes.2014.06.060   [Google Scholar]
  21. Mozafari B, Ranjbar AM, Shirani AR, and Mozafari A (2005). Reactive power management in a deregulated power system with considering voltage stability: Particle Swarm optimisation approach. In the CIRED 2005-18th International Conference and Exhibition on Electricity Distribution, IET, Turin, Italy: 1-4. https://doi.org/10.1049/cp:20051390   [Google Scholar]
  22. Raoufi H and Kalantar M (2009). Reactive power rescheduling with generator ranking for voltage stability improvement. Energy Conversion and Management, 50(4): 1129-1135. https://doi.org/10.1016/j.enconman.2008.11.013   [Google Scholar]
  23. Sánchez J, Visioli A, and Dormido S (2012). Event-based PID control. In: Vilanova R and Visioli A (Eds.), PID control in the Third Millennium: 495-526. Springer, Berlin, Germany. https://doi.org/10.1007/978-1-4471-2425-2_16   [Google Scholar]
  24. Seborg DE, Mellichamp DA, Edgar TF, and Doyle III FJ (2010). Process dynamics and control. John Wiley and Sons, Hoboken, USA.   [Google Scholar]
  25. Singh AK and Parida SK (2013). A multiple strategic evaluation for fault detection in electrical power system. International Journal of Electrical Power and Energy Systems, 48: 21-30. https://doi.org/10.1016/j.ijepes.2012.11.033   [Google Scholar]
  26. Sreejith S, Simon SP, and Selvan MP (2015). Analysis of FACTS devices on security constrained unit commitment problem. International Journal of Electrical Power and Energy Systems, 66: 280-293. https://doi.org/10.1016/j.ijepes.2014.10.049   [Google Scholar]
  27. Titare LS, Singh P, Arya LD, and Choube SC (2014). Optimal reactive power rescheduling based on EPSDE algorithm to enhance static voltage stability. International Journal of Electrical Power and Energy Systems, 63: 588-599. https://doi.org/10.1016/j.ijepes.2014.05.078   [Google Scholar]
  28. Tofighi M, Alizadeh M, Ganjefar S, and Alizadeh M (2015). Direct adaptive power system stabilizer design using fuzzy wavelet neural network with self-recurrent consequent part. Applied Soft Computing, 28: 514-526. https://doi.org/10.1016/j.asoc.2014.12.013   [Google Scholar]
  29. Vaahedi E, Mansour Y, Fuchs C, Granville S, Latore MDL, and Hamadanizadeh H (2001). Dynamic security constrained optimal power flow/var planning. IEEE Transactions on Power Systems, 16(1): 38-43. https://doi.org/10.1109/59.910779   [Google Scholar]
  30. Varadarajan M and Swarup KS (2008). Differential evolutionary algorithm for optimal reactive power dispatch. International Journal of Electrical Power and Energy Systems, 30(8): 435-441. https://doi.org/10.1016/j.ijepes.2008.03.003   [Google Scholar]
  31. Yang L and Entchev E (2014). Performance prediction of a hybrid microgeneration system using adaptive neuro-fuzzy inference system (ANFIS) technique. Applied Energy, 134: 197-203. https://doi.org/10.1016/j.apenergy.2014.08.022   [Google Scholar]
  32. Zhang W and Liu Y (2008). Multi-objective reactive power and voltage control based on fuzzy optimization strategy and fuzzy adaptive particle swarm. International Journal of Electrical Power and Energy Systems, 30(9): 525-532. https://doi.org/10.1016/j.ijepes.2008.04.005   [Google Scholar]
  33. Zhao B, Guo CX, and Cao YJ (2005). A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Transactions on Power Systems, 20(2): 1070-1078. https://doi.org/10.1109/TPWRS.2005.846064   [Google Scholar]