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


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.


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


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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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