The proposed work investigates the technological aspects of implementing a remote sensing application for photovoltaic devices. The hardware part of the implementation is built around a microcontroller equipped with wireless interface, accurate ADC converters and suitable circuitry for measurement of current, voltage and temperature directly on the device. A central unit running a Matlab server gathers information from the remote sensing devices through Wi-Fi. Exploiting the circuit model of the PV devices, instantaneous irradiance and shading can be computed. The distributed measurement of instantaneous shading can be used for important PV management actions, such as dynamic reconfiguration of the PV arrays and model-based simulations. Moreover, a short-term irradiance forecasting strategy is proposed based on neural predictors working on the spatially distributed shading measurements. The accuracy of the remote sensing device is validated experimentally using a PV device simulator.
Laudani, A., Lozito, G.M. (2019). Smart Distributed Sensing for Photovoltaic Applications. In Progress in Electromagnetics Research Symposium (pp.2908-2916). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/PIERS-Spring46901.2019.9017556].
Smart Distributed Sensing for Photovoltaic Applications
Laudani A.;Lozito G. M.
2019-01-01
Abstract
The proposed work investigates the technological aspects of implementing a remote sensing application for photovoltaic devices. The hardware part of the implementation is built around a microcontroller equipped with wireless interface, accurate ADC converters and suitable circuitry for measurement of current, voltage and temperature directly on the device. A central unit running a Matlab server gathers information from the remote sensing devices through Wi-Fi. Exploiting the circuit model of the PV devices, instantaneous irradiance and shading can be computed. The distributed measurement of instantaneous shading can be used for important PV management actions, such as dynamic reconfiguration of the PV arrays and model-based simulations. Moreover, a short-term irradiance forecasting strategy is proposed based on neural predictors working on the spatially distributed shading measurements. The accuracy of the remote sensing device is validated experimentally using a PV device simulator.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
