Solar Irradiance Estimation Using Kalman Filter

Abstract

This work presents a methodology to estimate solar irradiance using Kalman filter for systems with unknown inputs, an approach more adequate to system characteristics than the standard formulation of this tool. A system with photovoltaic panel, dc-dc converter and load was modeled and simulated in order to analyze the proposed methodology in situations of clear, almost clear and cloudy sky days. The proposed estimator and an analytical method are compared with respect to the ability to compute the irradiance and tested against uncertainties in modeling parameters and noise in the voltage and current measurements of the system. The results show that, through a single sensor, the developed methodology allows to estimate and filter not only solar irradiance, but also output current of photovoltaic system and output voltage of converter. This brings benefits in reducing costs with sensors, allows real-time measurements and avoids propagating noisy measures in the management of a solar system.

Solar Irradiation Evaluation through GIS Analysis Based on Grid Resolution and a Mathematical Model: A Case Study in Northeast Mexico

“…These results have shown significant opportunity areas in some empirical models [7]. This opens the possibility to other radiation estimation approaches such as deep learning, which has been applied to a multi-layer perceptron (MLPs) method to estimate horizontal daily solar irradiation [8], bayesian model averaging and machine learning [9], or more theory-oriented control systems like Kalman filters [10].…”