Solar Irradiance Estimation Using Kalman Filter
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Solar Irradiance Estimation Using Kalman Filter Vinícius Souza Madureira1,2
· Thiago Pereira das Chagas1,2
· Gildson Queiroz de Jesus1,2
Received: 3 October 2019 / Revised: 26 July 2020 / Accepted: 14 September 2020 © Brazilian Society for Automatics–SBA 2020
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. Keywords Photovoltaic systems · Irradiance estimation · Kalman filter · dc–dc converter · State space
1 Introduction The growing demand for electricity calls for even more studies and the development of clean and renewable energy sources. In this field, photovoltaic (PV) panels are a very important alternative because of their low environmental impact and long lifespan. However, PV generation is an intermittent source, even during the day, as solar irradiance and temperature directly affect panel efficiency. Accurate information about these variables is essential to know the potential of solar energy generation in a given location (Martins and Pereira 2011; Pereira et al. 2017). Such information can also reveal the best way used to manage real-time generation systems through applications such as Maximum Power Point Tracking (MPPT) (Chikh and Chandra 2015; Scolari et al. 2017) and control hybrid power generation systems (Ding et al. 2015; Mohamed and
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Vinícius Souza Madureira [email protected]
1
Postgraduate Program in Science and Technology Computational Modeling (PPGMC), Department of Exact Science and Technology (DCET), State University of Santa Cruz (UESC), Ilhéus, BA, Brazil
2
Mechatronics Laboratory, Department of Exact Science and Technology (DCET), State University of Santa Cruz (UESC), Ilhéus, BA, Brazil
Mohammed 2013). However, in addition to being associated with the Earth’s translation and rotation movements, the solar resource depends directly on local meteorological factors and is thus highly variable (Pereira et al. 2017). The most reliable way to know in real time the behavior of a location’s solar resource is to use pyranometers (Martins and Pereira 2011)—measuring instruments that capture global irradiance. This sensor, however, re
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