Downloads provided by UsageCounts
The dynamic reconfiguration and maximum power point tracking in large-scale photovoltaic (PV) systems require a large number of voltage and current sensors. In particular, the reconfiguration process requires a pair of voltage/current sensors for each panel, which introduces costs, increases size and reduces reliability of the installation. A suitable solutions for reducing the number of sensors is to adopt image-based solution to estimate the electrical characteristics of the PV panels, but the lack of reliable data with large diversity of irradiance and shading conditions is a major problem in this topic. Therefore, this paper presents dataset correlating RGB images and electrical data of PV panels with different irradiance and shading conditions. The dataset was designed to support the design of image-based estimators of electrical data, which could be used to replace large arrays of sensors. The paper also describes the measurement platform used to collect the data, which helps to replicate the experiments in different geographical locations.
photovoltaic, partial shading, image-based estimation, current vs. voltage characteristic
photovoltaic, partial shading, image-based estimation, current vs. voltage characteristic
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 136 | |
| downloads | 158 |

Views provided by UsageCounts
Downloads provided by UsageCounts