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| Format: | Artículo científico |
| Sprache: | en |
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Escuela de Ingeniería de Antioquia
2013
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| Online-Zugang: | https://www.redalyc.org/articulo.oa?id=149229801015 |
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| _version_ | 1866569645504331776 |
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| author | Andrés Julián Saavedra Montes |
| author_facet | Andrés Julián Saavedra Montes |
| contents | Adaptive maximum power point tracking algorithm for multi-variable applications in photovoltaic arrays Andrés Julián Saavedra Montes Carlos Andrés Ramos Paja Luz Adriana Trejos Grisales Ingeniería Efficiency Adaptive Algorithm Maximum Power Point Photovoltaic Generation Classical algorithms for multi-variable photovoltaic systems use fixed-size perturbations, which does not optimize the produced power in both steady-state and transient conditions. Therefore, an adaptive maximum power point tracking algorithm for photovoltaic systems is proposed in this paper to improve the power generation in both transient and steady-state conditions. The proposed algorithm only uses a single pair of current/voltage sensors to reach the global maximum available power, which contrast with the high number of sensors required by others distributed maximum power point tracking solutions. The algorithm recognizes the voltage pattern exhibited by PV system in both steady-state and transient conditions to adapt the size perturbations accordingly: in steady-state conditions reduces the perturbation size to minimize the power loses, while in transient conditions increases the perturbation size to speed-up the tracking of the new operating point. Finally, the adaptive multi-variable perturb and observe algorithm is validated by means of simulation using detailed models. 2013 artículo científico 1794-1237 https://www.redalyc.org/articulo.oa?id=149229801015 en http://www.redalyc.org/revista.oa?id=1492 Revista EIA application/pdf Escuela de Ingeniería de Antioquia Revista EIA (Colombia) Num.20 Vol.10 |
| format | Artículo científico |
| id | redalyc_149229801015 |
| language | en |
| publishDate | 2013 |
| publisher | Escuela de Ingeniería de Antioquia |
| spellingShingle | Adaptive maximum power point tracking algorithm for multi-variable applications in photovoltaic arrays Andrés Julián Saavedra Montes Ingeniería Efficiency Adaptive Algorithm Maximum Power Point Photovoltaic Generation Adaptive maximum power point tracking algorithm for multi-variable applications in photovoltaic arrays Andrés Julián Saavedra Montes Carlos Andrés Ramos Paja Luz Adriana Trejos Grisales Ingeniería Efficiency Adaptive Algorithm Maximum Power Point Photovoltaic Generation Classical algorithms for multi-variable photovoltaic systems use fixed-size perturbations, which does not optimize the produced power in both steady-state and transient conditions. Therefore, an adaptive maximum power point tracking algorithm for photovoltaic systems is proposed in this paper to improve the power generation in both transient and steady-state conditions. The proposed algorithm only uses a single pair of current/voltage sensors to reach the global maximum available power, which contrast with the high number of sensors required by others distributed maximum power point tracking solutions. The algorithm recognizes the voltage pattern exhibited by PV system in both steady-state and transient conditions to adapt the size perturbations accordingly: in steady-state conditions reduces the perturbation size to minimize the power loses, while in transient conditions increases the perturbation size to speed-up the tracking of the new operating point. Finally, the adaptive multi-variable perturb and observe algorithm is validated by means of simulation using detailed models. 2013 artículo científico 1794-1237 https://www.redalyc.org/articulo.oa?id=149229801015 en http://www.redalyc.org/revista.oa?id=1492 Revista EIA application/pdf Escuela de Ingeniería de Antioquia Revista EIA (Colombia) Num.20 Vol.10 |
| title | Adaptive maximum power point tracking algorithm for multi-variable applications in photovoltaic arrays |
| topic | Ingeniería Efficiency Adaptive Algorithm Maximum Power Point Photovoltaic Generation |
| url | https://www.redalyc.org/articulo.oa?id=149229801015 |