Micro-genetic algorithms for detecting and classifying electric power disturbances |
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Authors: | Jaen-Cuellar Arturo Yosimar Morales-Velazquez Luis Romero-Troncoso Rene de Jesus Moriigo-Sotelo Daniel Osornio-Rios Roque Alfredo |
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Affiliation: | 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 ;2.HSPdigital – CA Telematica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle km 3.5+1.8, Palo Blanco, 36700, Salamanca, GTO, Mexico ;3.Electrical Engineering Department, EII, Universidad de Valladolid, C/Paseo del Cauce 59, 47011, Valladolid, Spain ; |
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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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