An Interval-Arithmetic-Based Approach to the Parametric Identification of the Single-Diode Model of Photovoltaic Generators

Abstract

Parametric identification of the single diode model of a photovoltaic generator is a key element in simulation and diagnosis. Parameters’ values are often determined by using experimental data the modules manufacturers provide in the data sheets. In outdoor applications, the parametric identification is instead performed by starting from the current vs. voltage curve acquired in non-standard operating conditions. This paper refers to this latter case and introduces an approach based on the use of interval arithmetic. Photovoltaic generators based on crystalline silicon cells are considered: they are modeled by using the single diode model, and a divide-and-conquer algorithm is used to contract the initial search space up to a small hyper-rectangle including the identified set of parameters. The proposed approach is validated by using experimental data measured in outdoor conditions. The information provided by the approach, in terms of parametric sensitivity and of correlation between current variations and drifts of the parameters values, is discussed. The results are analyzed in view of the on-site application of the proposed approach for diagnostic purposes.

Improving the Convergence of Interval Single-Step Method for Simultaneous Approximation of Polynomial Zeros

“…Interval arithmetic also plays a significant role in enhancing the power of higher performance computing, such as GPUs [2]. Furthermore, in the era of data science and artificial intelligence, many scholars and practitioners from various backgrounds have incorporated the interval analysis concepts into their existing models or methods in order to investigate uncertainty propagation in specific data or systems [3][4][5]. These applications have led to increased attention and more rigorous studies on interval methods over the recent years.…”

On the Normalization of Interval Data

“…Normalization of input data is an important method in many applications involving numerical data. When such input data contain impreciseness they are normally represented by intervals, which can be operated by various available interval arithmetics-for example, [1][2][3]. There is no reference relating normalization and interval arithmetics.…”