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Micro-genetic algorithms for detecting and classifying electric power disturbances

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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.

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Authors and Affiliations

  1. HSPdigital – CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, 76807, San Juan del Río, QRO, Mexico

    Arturo Yosimar Jaen-Cuellar, Luis Morales-Velazquez & Roque Alfredo Osornio-Rios

  2. HSPdigital – CA Telematica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle km 3.5+1.8, Palo Blanco, 36700, Salamanca, GTO, Mexico

    Rene de Jesus Romero-Troncoso

  3. Electrical Engineering Department, EII, Universidad de Valladolid, C/Paseo del Cauce 59, 47011, Valladolid, Spain

    Daniel Moriñigo-Sotelo

Authors
  1. Arturo Yosimar Jaen-Cuellar
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  2. Luis Morales-Velazquez
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  3. Rene de Jesus Romero-Troncoso
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  5. Roque Alfredo Osornio-Rios
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Correspondence to Roque Alfredo Osornio-Rios.

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Jaen-Cuellar, A.Y., Morales-Velazquez, L., Romero-Troncoso, R.d. et al. Micro-genetic algorithms for detecting and classifying electric power disturbances. Neural Comput & Applic 28 (Suppl 1), 379–392 (2017). https://doi.org/10.1007/s00521-016-2355-z

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