21 research outputs found
New method for analytical photovoltaic parameters identification: meeting manufacturer’s datasheet for different ambient conditions
At present, photovoltaic energy is one of the most important renewable energy sources. The demand for solar panels has been continuously growing, both in the industrial electric sector and in the private sector. In both cases the analysis of the solar panel efficiency is extremely important in order to maximize the energy production. In order to have a more efficient photovoltaic system, the most accurate understanding of this system is required. However, in most of the cases the only information available in this matter is reduced, the experimental testing of the photovoltaic device being out of consideration, normally for budget reasons. Several methods, normally based on an equivalent circuit model, have been developed to extract the I-V curve of a photovoltaic device from the small amount of data provided by the manufacturer. The aim of this paper is to present a fast, easy, and accurate analytical method, developed to calculate the equivalent circuit parameters of a solar panel from the only data that manufacturers usually provide. The calculated circuit accurately reproduces the solar panel behavior, that is, the I-V curve. This fact being extremely important for practical reasons such as selecting the best solar panel in the market for a particular purpose, or maximize the energy extraction with MPPT (Maximum Peak Power Tracking) methods
Artificial intelligence for photovoltaic systems
Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods
Simultaneous measurement of film and substrate optical parameters from multiple sample single wavelength ellipsometric data
A procedure has been developed for the accurate measurement of film and substrate optical
parameters from the multiple
sample single-wavelength ellipsometric data. The dimensional
reduction of the unknowns from
newly formulated ellipsometric functions, the root selection and
the thickness-dependent integer
deduction enhance the rapidity of finding solutions and the
convergence from a wide range of
initial guesses while avoiding undesirable solutions. An error
analysis carried out shows that the
procedure is very resistant to the propagation of angular errors
and allows the estimation of optimum film
thickness ranges under which the parameters can be
accurately found. The standard SiO2/Si
structure is particularly studied using the procedure that
is further illustrated with the experimental
data on Ni/BK7-glass structures. The SiO2 film
refractive index and thickness are thus shown to be accurately determined when sought along
with the substrate optical constants. Moreover, the film and substrate real indexes are not altered
in the presence of an interface layer between the film and the substrate while its existence is
indicated by a systematic lowering of the Si substrate extinction coefficient. The procedure can
be efficiently used in the continuous real-time optical characterization of films growing on
substrates
Influence of parasitic resistances on the mismatch relative power loss of solar cell modules
Peak power available from a photovoltaic module is reduced by the cell-to-cell variations in the photogenerated current, which originate from the manufacturing process. In order to enhance the array output power, the cells are generally sorted before being placed in modules. A computer analysis performed allows to assess the mismatch relative power loss (mmRPL), particularly the influence of the parasitic shunt conductance. Empirical expressions and a method of evaluation are proposed for the mmRPL of a series-connected module and, to a limited extent, for mmRPL of a parallel-connected module.La puissance crête fournie par un module photovoltaïque est réduite par la dispersion des valeurs des courants photogénérés des cellules, ceci est dû essentiellement aux procédés de fabrication. Pour améliorer la puissance de sortie, les cellules sont triées en vue de réduire cette dispersion avant d'être placées en série pour former un module. Une analyse assistée par calculateur a été effectuée pour étudier la perte relative de puissance due à la dispersion (mmRPL) et, en particulier, l'influence de la conductance shunt parasite. Une expression empirique et une méthode simple d'évaluation du facteur de perte mmRPL sont proposées pour un module connecté en série et, dans une certaine mesure, ces résultats sont étendus aux panneaux constitués de cellules mises en parallèle