Abstract
The central problem tackled in this article is the susceptibility of the solar modules to dirt that culminates in losses in energy generation or even physical damage. In this context, a solution is presented to enable the estimates of dirt losses in photovoltaic generation units. The proposed solution is based on the mathematical modeling of the solar cells and predictive modeling concepts. A device was designed and developed to acquire data from the photovoltaic unit; process them based on a predictive model, and send loss estimates in the generation unit to a web server to help in decision-making support. The results demonstrated the real applicability of the system to estimate losses due to dirt or electrical mismatches in photovoltaic plants.
Highlights
Photovoltaic conversion is the direct energy transformation from solar radiation into electrical energy through the photovoltaic effect
The electrical energy obtained can be injected into the electrical grid by some power electronics converter, giving rise to the mini and micro photovoltaic generation systems
The energy losses model predictive control (MPC) estimator is based on the mathematical model of the solar cell for calculating Gac, as well as on the predictive model for estimating an approximation for Rs
Summary
Photovoltaic conversion is the direct energy transformation from solar radiation into electrical energy through the photovoltaic effect. Since the enactment of the resolution of the National Electric Energy Agency (ANEEL), number 482/2012, which regulated the mini and microgeneration distributed systems in Brazil, the usage of photovoltaic solar energy has expanded, achieving thousands of new installations each year [1,2]. It is well-known that photovoltaic solar energy presents financial and ecological benefits associated with the strident reduction in costs for implementing generation systems. This fact is corroborated by international agencies, which predict that photovoltaic energy increases from 2% of the world energy matrix in 2018 to 25% in 2050 [3]
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