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Neural Computing and Applications

, Volume 28, Supplement 1, pp 379–392 | Cite as

Micro-genetic algorithms for detecting and classifying electric power disturbances

  • Arturo Yosimar Jaen-Cuellar
  • Luis Morales-Velazquez
  • Rene de Jesus Romero-Troncoso
  • Daniel Moriñigo-Sotelo
  • Roque Alfredo Osornio-RiosEmail author
Original Article

Abstract

The power quality analysis represents an important aspect in the overall society welfare. The analysis of power disturbances in electrical systems is typically performed in two steps: disturbance detection and disturbance classification. Disturbance detection is usually made through space transform techniques, and their classification is usually performed through artificial intelligence methods. The problem with those approaches is the adequate selection of parameters for these techniques. Due to the advantages of a variant scheme known as the micro-genetic algorithms, in this investigation, a new methodology to directly detect and classify electrical disturbances in one step is developed. The proposed approach is validated through synthetic signals and experimental test on real data, and the obtained results are compared with the particle swarm optimization method in order to show the effectiveness of this methodology.

Keywords

Genetic algorithms Micro-genetic algorithms Power quality analysis Power quality disturbances 

References

  1. 1.
    Ramesh M, Laxmi AJ (2012) Fault identification in HVDC using artificial intelligence—recent trends and perspective. In: International conference on power, signals, controls and computation (EPSCICON), pp 1–6. doi: 10.1109/EPSCICON.2012.6175256
  2. 2.
    Wulandhari LA, Wibowo A, Desa MI (2015) Condition diagnosis of multiple bearings using adaptive operator probabilities in genetic algorithms and back propagation neural networks. Neural Comput Appl 26:57–65. doi: 10.1007/s00521-014-1698-6 CrossRefGoogle Scholar
  3. 3.
    Raja MAZ (2014) Solution of the one-dimensional Bratu equation arising in the fuel ignition model using ANN optimised with PSO and SQP. Connect Sci 26(3):195–214. doi: 10.1080/09540091.2014.907555 CrossRefGoogle Scholar
  4. 4.
    Khan JA, Raja MAZ, Rashidi MM, Syam MI, Wazwaz AM (2015) Nature-inspired computing approach for solving non-linear singular Emden-Fowler problem arising in electromagnetic theory. Connect Sci 27(4):377–396. doi: 10.1080/09540091.2015.1092499 CrossRefGoogle Scholar
  5. 5.
    Raja MAZ (2014) Stochastic numerical techniques for solving Troesch’s problem. Inform Sci 279:860–873. doi: 10.1016/j.ins.2014.04.036 MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Wan C, Zhu Z, Zhong W (2012) Genetic algorithms for designing energy-efficient optical transport networks with mixed regenerator placement. In: IEEE international conference on communications (ICC), pp 3015–3019. doi: 10.1109/ICC.2012.6363777
  7. 7.
    Rao SS (2009) Engineering optimization theory and practice. Wiley, New York, pp 693–730CrossRefGoogle Scholar
  8. 8.
    Raja MAZ, Sabir Z, Mehmood N, Aidarous ES, Khan JA (2015) Design of stochastic solvers base on genetic algorithms for solving nonlinear equations. Neural Comp Appl 26:1–23. doi: 10.1007/s00521-014-1676-z CrossRefGoogle Scholar
  9. 9.
    Ding S, Zhang Y, Chen J, Jia W (2013) Research on using genetic algorithms to optimize Elman neural networks. Neural Comp Appl 23:293–297. doi: 10.1007/s00521-012-0896-3 CrossRefGoogle Scholar
  10. 10.
    Jaen-Cuellar AY, Romero-Troncoso RJ, Morales-Velazquez L, Osornio-Rios RA (2013) PID-Controller tuning optimization with genetic algorithms in servo systems. Int J Adv Robot Syst 10:1–14. doi: 10.5772/56697 CrossRefGoogle Scholar
  11. 11.
    Jaen-Cuellar AY, Morales-Velazquez L, Romero-Troncoso RJ, Osornio-Rios RA (2015) FPGA-based embedded system architecture for micro-genetic algorithms applied to parameters optimization in motion control. Adv Electr Comput Eng 15:23–32. doi: 10.4316/AECE.2015.01004 CrossRefGoogle Scholar
  12. 12.
    Raja MAZ, Farooq U, Chaudhary NI, Wazwaz AM (2016) Stochastic numerical solver for nanofluidic problems containing multi-walled carbon nanotubes. Appl Soft Comput 38:561–582. doi: 10.1016/j.asoc.2015.10.015 CrossRefGoogle Scholar
  13. 13.
    Raja MAZ, Khan JA, Haroon T (2015) Stochastic numerical treatment for thin film flow of third grade fluid using unsupervised neural networks. J Taiwan Inst Chem Eng 48:26–39. doi: 10.1016/j.jtice.2014.10.018 CrossRefGoogle Scholar
  14. 14.
    Raja MAZ, Shah FH, Khan AA, Khan NA (2015) Design of bio-inspired computational intelligence technique for solving steady thin film flow of Johnson–Segalman fluid on vertical cylinder for drainage problems. J Taiwan Inst Chem Eng. doi: 10.1016/j.jtice.2015.10.020 Google Scholar
  15. 15.
    Raja MAZ, Samar R, Haroon T, Shah SM (2015) Unsupervised neural network model optimized with evolutionary computations for solving variants of nonlinear MHD Jeffery–Hamel problem. Appl Math Mech 36(12):1611–1638. doi: 10.1007/s10483-015-2000-6 MathSciNetCrossRefGoogle Scholar
  16. 16.
    Golea NE-H, Melkemi KE, Melkemi M (2011) A novel multi-objective genetic algorithm optimization for blind RGB color image watermarking. In: Seventh international conference on signal-image technology and internet-based systems (SITIS), pp 306–313. doi: 10.1109/SITIS.2011.16
  17. 17.
    Wang S, Xu Z (2013) Increasing the SSO damping effectiveness of IMDU by raising its operating frequency and optimizing its parameters. IEEE Trans Power Syst 28:3134–3144. doi: 10.1109/TPWRS.2012.2234145 CrossRefGoogle Scholar
  18. 18.
    Wang MH, Tseng YF (2011) A novel analytic method of power quality using extension genetic algorithm and wavelet transform. Expert Syst Appl 38:12491–12496. doi: 10.1016/j.eswa.2011.04.032 CrossRefGoogle Scholar
  19. 19.
    Sanchez P, Montoya FG, Manzano-Agugliaro F, Gil C (2013) Genetic algorithm for S-transform optimization in the analysis and classification of electrical signal perturbations. Expert Syst Appl 40:6766–6777. doi: 10.1016/j.eswa.2013.06.055 CrossRefGoogle Scholar
  20. 20.
    Baier CR, Espinoza JR, Rivera M, Munoz JA, Wu B, Melin PE, Yaramasu V (2014) Improving power quality in cascade multilevel converters based on single-phase nonregenerative power cells. IEEE Trans Ind Electron 61:4498–4509. doi: 10.1109/TIE.2013.2289866 CrossRefGoogle Scholar
  21. 21.
    Javadi A, Al-Haddad K (2015) A single-phase active device for power quality improvement of electrified transportation. IEEE Trans Ind Electron 62:3033–3041. doi: 10.1109/TIE.2015.2402639 CrossRefGoogle Scholar
  22. 22.
    Honrubia-Escribano A, Gómez-Lázaro E, Molina-Garcia A, Martín-Martínez S (2014) Load influence on the response of AC-contactors under power quality disturbances. Int J Electr Power 63:846–854. doi: 10.1016/j.ijepes.2014.06.056 CrossRefGoogle Scholar
  23. 23.
    Valtierra-Rodriguez M, Romero-Troncoso RJ, Osornio-Rios RA, Garcia-Perez A (2014) Detection and classification of single and combined power quality disturbances using neural networks. IEEE Trans Ind Electron 61:2473–2482. doi: 10.1109/TIE.2013.2272276 CrossRefGoogle Scholar
  24. 24.
    Torabian-Esfahani M, Hosseinian SH, Vahidi B (2015) A new optimal approach for improvement of active power filter using FPSO for enhancing power quality. Int J Electr Power 69:188–199. doi: 10.1016/j.ijepes.2014.12.078 CrossRefGoogle Scholar
  25. 25.
    Ji TY, Wu QH, Jiang L, Tang WH (2011) Disturbance detection, location and classification in phase space. IET Gener Transm Distrib 5:257–265. doi: 10.1049/iet-gtd.2010.0254 CrossRefGoogle Scholar
  26. 26.
    Hajian M, Foroud AA, Abdoos AA (2014) New automated power quality recognition system for online/offline monitoring. Neurocomputing 128:389–406. doi: 10.1016/j.neucom.2013.08.026 CrossRefGoogle Scholar
  27. 27.
    Saini MK, Kapoor R (2012) Classification of power quality events—a review. Int J Electr Power 43:11–19. doi: 10.1016/j.ijepes.2012.04.045 CrossRefGoogle Scholar
  28. 28.
    Mahela OP, Shaik AG, Gupta N (2015) A critical review of detection and classification of power quality events. Renew Sustain Energy Rev 41:495–505. doi: 10.1016/j.rser.2014.08.070 CrossRefGoogle Scholar
  29. 29.
    Tse NCF, Chan JYC, Wing-Hong L, Poon JTY, Lai LL (2012) Real-time power-quality monitoring with hybrid sinusoidal and lifting wavelet compression algorithm. IEEE Trans Power Deliv 27:1718–1726. doi: 10.1109/TPWRD.2012.2201510 CrossRefGoogle Scholar
  30. 30.
    Soo-Hwan C, Chang-Hyun P, Han J, Jang G (2012) A waveform distortion evaluation method based on a simple half-cycle RMS calculation. IEEE Trans Power Deliv 27:1461–1467. doi: 10.1109/TPWRD.2012.2190304 CrossRefGoogle Scholar
  31. 31.
    Chang GW, Min-Fu S, Yi-Ying C, Yi-Jie L (2014) A hybrid wavelet transform and neural-network-based approach for modelling dynamic voltage-current characteristics of electric arc furnace. IEEE Trans Power Deliv 29:815–824. doi: 10.1109/TPWRD.2013.2280397 CrossRefGoogle Scholar
  32. 32.
    De Yong D, Bhowmik S, Magnago F (2015) An effective power quality classifier using wavelet transform and support vector machines. Expert Syst Appl 42:6075–6081. doi: 10.1016/j.eswa.2015.04.002 CrossRefGoogle Scholar
  33. 33.
    Dehghani H, Vahidi B, Naghizadeh RA, Hosseinian SH (2013) Power quality disturbance classification using a statistical and wavelet-based Hidden Markov Model with Dempster–Shafer algorithm. Int J Electr Power 47:368–377. doi: 10.1016/j.ijepes.2012.11.005 CrossRefGoogle Scholar
  34. 34.
    Latran MB, Teke A (2015) A novel wavelet transform based voltage sag/swell detection algorithm. Int J Electr Power 71:131–139. doi: 10.1016/j.ijepes.2015.02.040 CrossRefGoogle Scholar
  35. 35.
    Eristi H, Yildirim O, Eristi B, Demir Y (2014) Automatic recognition system of underlying causes of power quality disturbances based on S-transform and extreme learning machine. Int J Electr Power 61:553–562. doi: 10.1016/j.ijepes.2014.04.010 CrossRefGoogle Scholar
  36. 36.
    Granados-Lieberman D, Valtierra-Rodriguez M, Morales-Hernandez LA, Romero-Troncoso RJ, Osornio-Rios RA (2013) A Hilbert transform-based smart sensor for detection, classification, and quantification of power quality disturbances. Sensors 13:5507–5527. doi: 10.3390/s130505507 CrossRefGoogle Scholar
  37. 37.
    Afroni MJ, Sutanto D, Stirling D (2013) Analysis of nonstationary power-quality waveforms using iterative Hilbert Huang transform and SAX algorithm. IEEE Trans Power Deliv 28:2134–2144. doi: 10.1109/TPWRD.2013.2264948 CrossRefGoogle Scholar
  38. 38.
    Abdelsalam AA, Eldesouky AA, Sallam AA (2012) Classification of power system disturbances using linear Kalman filter and fuzzy-expert system. Int J Electr Power 43:688–695. doi: 10.1016/j.ijepes.2012.05.052 CrossRefGoogle Scholar
  39. 39.
    Granados-Lieberman D, Romero-Troncoso RJ, Cabal-Yepez E, Osornio-Rios RA, Franco-Gasca LA (2009) A real-time smart sensor for high-resolution frequency estimation in power systems. Sensors 9:7412–7429. doi: 10.3390/s90907412 CrossRefGoogle Scholar
  40. 40.
    Biswal B, Biswal MK, Dash PK, Mishra S (2013) Power quality event characterization using support vector machine and optimization using advanced immune algorithm. Neurocomputing 103:75–86. doi: 10.1016/j.neucom.2012.08.031 CrossRefGoogle Scholar
  41. 41.
    Abdelsalam AA, Eldesouky AA, Sallam AA (2012) Characterization of power quality disturbances using hybrid technique of linear Kalman filter and fuzzy-expert system. Electr Power Syst Res 83:41–50. doi: 10.1016/j.epsr.2011.09.018 CrossRefGoogle Scholar
  42. 42.
    Cabal-Yepez E, Valtierra-Rodriguez M, Romero-Troncoso RJ, Garcia-Perez A, Osornio-Rios RA, Miranda-Vidales H, Alvarez-Salas R (2012) FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors. Mech Syst Signal Process 30:123–130. doi: 10.1016/j.ymssp.2012.01.021 CrossRefGoogle Scholar
  43. 43.
    IEEE Recommended Practices for Monitoring Electric Power Quality, IEEE Std. 1159–2009, 2009Google Scholar
  44. 44.
    Voltage Characteristics of Electricity Supplied by Public Distribution Systems, Eur. Std. EN 50160, 2002Google Scholar

Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Arturo Yosimar Jaen-Cuellar
    • 1
  • Luis Morales-Velazquez
    • 1
  • Rene de Jesus Romero-Troncoso
    • 2
  • Daniel Moriñigo-Sotelo
    • 3
  • Roque Alfredo Osornio-Rios
    • 1
    Email author
  1. 1.HSPdigital – CA Mecatronica, Facultad de IngenieriaUniversidad Autonoma de QueretaroSan Juan del RíoMexico
  2. 2.HSPdigital – CA Telematica, DICISUniversidad de GuanajuatoSalamancaMexico
  3. 3.Electrical Engineering Department, EIIUniversidad de ValladolidValladolidSpain

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