Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation
Renewable generation sources like photovoltaic plants are weather dependent and it is hard to predict their behavior. This work proposes a methodology for obtaining a parameterized model that estimates the generated power in a photovoltaic generation system. The proposed methodology uses a genetic a...
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MDPI AG
2020-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/2/542 |
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| author | David A. Elvira-Ortiz Arturo Y. Jaen-Cuellar Daniel Morinigo-Sotelo Luis Morales-Velazquez Roque A. Osornio-Rios Rene de J. Romero-Troncoso |
| author_facet | David A. Elvira-Ortiz Arturo Y. Jaen-Cuellar Daniel Morinigo-Sotelo Luis Morales-Velazquez Roque A. Osornio-Rios Rene de J. Romero-Troncoso |
| author_sort | David A. Elvira-Ortiz |
| collection | DOAJ |
| description | Renewable generation sources like photovoltaic plants are weather dependent and it is hard to predict their behavior. This work proposes a methodology for obtaining a parameterized model that estimates the generated power in a photovoltaic generation system. The proposed methodology uses a genetic algorithm to obtain the mathematical model that best fits the behavior of the generated power through the day. Additionally, using the same methodology, a mathematical model is developed for harmonic distortion estimation that allows one to predict the produced power and its quality. Experimentation is performed using real signals from a photovoltaic system. Eight days from different seasons of the year are selected considering different irradiance conditions to assess the performance of the methodology under different environmental and electrical conditions. The proposed methodology is compared with an artificial neural network, with the results showing an improved performance when using the genetic algorithm methodology. |
| first_indexed | 2024-12-10T21:29:34Z |
| format | Article |
| id | doaj.art-797824d3f94f4d44972802d9ca551d44 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-10T21:29:34Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-797824d3f94f4d44972802d9ca551d442022-12-22T01:32:52ZengMDPI AGApplied Sciences2076-34172020-01-0110254210.3390/app10020542app10020542Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic GenerationDavid A. Elvira-Ortiz0Arturo Y. Jaen-Cuellar1Daniel Morinigo-Sotelo2Luis Morales-Velazquez3Roque A. Osornio-Rios4Rene de J. Romero-Troncoso5HSPdigital—CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Col. San Cayetano, San Juan del Rio C. P. 76807, Queretaro, MexicoHSPdigital—CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Col. San Cayetano, San Juan del Rio C. P. 76807, Queretaro, MexicoHSPdigital—Research Group ADIRE, University of Valladolid, UVa., Paseo del Cauce, 59, 47011 Valladolid, SpainHSPdigital—CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Col. San Cayetano, San Juan del Rio C. P. 76807, Queretaro, MexicoHSPdigital—CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Col. San Cayetano, San Juan del Rio C. P. 76807, Queretaro, MexicoHSPdigital—CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Col. San Cayetano, San Juan del Rio C. P. 76807, Queretaro, MexicoRenewable generation sources like photovoltaic plants are weather dependent and it is hard to predict their behavior. This work proposes a methodology for obtaining a parameterized model that estimates the generated power in a photovoltaic generation system. The proposed methodology uses a genetic algorithm to obtain the mathematical model that best fits the behavior of the generated power through the day. Additionally, using the same methodology, a mathematical model is developed for harmonic distortion estimation that allows one to predict the produced power and its quality. Experimentation is performed using real signals from a photovoltaic system. Eight days from different seasons of the year are selected considering different irradiance conditions to assess the performance of the methodology under different environmental and electrical conditions. The proposed methodology is compared with an artificial neural network, with the results showing an improved performance when using the genetic algorithm methodology.https://www.mdpi.com/2076-3417/10/2/542genetic algorithmsparameter estimationphotovoltaic systemspower qualitytotal harmonic distortion |
| spellingShingle | David A. Elvira-Ortiz Arturo Y. Jaen-Cuellar Daniel Morinigo-Sotelo Luis Morales-Velazquez Roque A. Osornio-Rios Rene de J. Romero-Troncoso Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation Applied Sciences genetic algorithms parameter estimation photovoltaic systems power quality total harmonic distortion |
| title | Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation |
| title_full | Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation |
| title_fullStr | Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation |
| title_full_unstemmed | Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation |
| title_short | Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation |
| title_sort | genetic algorithm methodology for the estimation of generated power and harmonic content in photovoltaic generation |
| topic | genetic algorithms parameter estimation photovoltaic systems power quality total harmonic distortion |
| url | https://www.mdpi.com/2076-3417/10/2/542 |
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