MPPT in PV systems under partial shading conditions using artificial vision
Maximum power point tracking (MPPT) algorithms should track and extract the maximum power from photovoltaic (PV) systems under any environmental conditions. Most of conventional MPPT methods are able to reach the maximum point when there is only one peak in the P–V characteristic curve but they fail...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad de Huelva (UHU) |
| Repositorio: | Arias Montano. Repositorio Institucional de la Universidad de Huelva |
| Idioma: | inglés |
| OAI Identifier: | oai:ariasmontano.uhu.es:10272/23017 |
| Acceso en línea: | https://hdl.handle.net/10272/23017 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial vision Backstepping control DC/DC converter MPPT algorithm Photovoltaic system 33 Ciencias Tecnológicas |
| Sumario: | Maximum power point tracking (MPPT) algorithms should track and extract the maximum power from photovoltaic (PV) systems under any environmental conditions. Most of conventional MPPT methods are able to reach the maximum point when there is only one peak in the P–V characteristic curve but they fail when the solar cells are affected by partial shading conditions due to the fact that multiple peaks appear in the P–V curve. Thus, a local maximum may be reached instead of the global peak. In this work, a new method to accomplish the maximum power point (MPP) under partial shading conditions using artificial vision is presented. The artificial vision uses a webcam to identify in real time the shadow irradiance and provide the reference voltage that supplies the maximum power, regardless of the number of peaks that the P–V curve presents. Then, the reference voltage is used by a robust and non-linear control, the backstepping controller, to regulate the DC/DC converter input voltage and to guarantee the PV modules maximum energy extraction. Experimental tests carried out outdoor validate the proposed method, obtaining a MPP tracking efficiency that ranges from 98.1% to 99.6%. |
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