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

Stability theory of stochastic models in opinion dynamics

We consider a certain class of nonlinear maps that preserve the probability simplex, i.e., stochastic maps, which are inspired by the DeGroot-Friedkin model of belief/opinion propagation over influence networks. The corresponding dynamical models describe the evolution of the probability distribution of interacting species. Such models where the probability transition mechanism depends nonlinearly on the current state are often referred to as nonlinear Markov chains. In this paper, we develop stability results and study the behavior of representative opinion models. The stability certificates are based on the contractivity of the nonlinear evolution in the ℓ1-metric. We apply the theory to two types of opinion models where the adaptation of the transition probabilities to the current state is exponential and linear - both of these can display a wide range of behaviors. We discuss continuous-time and other generalizations

Stability theory of stochastic models in opinion dynamics

We consider a certain class of nonlinear maps that preserve the probability simplex, i.e., stochastic maps, which are inspired by the DeGroot-Friedkin model of belief/opinion propagation over influence networks. The corresponding dynamical models describe the evolution of the probability distribution of interacting species. Such models where the probability transition mechanism depends nonlinearly on the current state are often referred to as nonlinear Markov chains. In this paper, we develop stability results and study the behavior of representative opinion models. The stability certificates are based on the contractivity of the nonlinear evolution in the ℓ1-metric. We apply the theory to two types of opinion models where the adaptation of the transition probabilities to the current state is exponential and linear - both of these can display a wide range of behaviors. We discuss continuous-time and other generalizations