A computational intelligence approach to solar panel modelling
| Autor: | Stefano Ferrari, Loredana Cristaldi, Vincenzo Piuri, Massimo Lazzaroni, Sergio Toscani, Marco Faifer, Ayşe Salman |
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| Přispěvatelé: | Doğuş Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, TR143709, Salman, Ayşe |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Engineering
Maximum power principle Testing Photovoltaic Energy Computational intelligence Predictive models Artificial Intelligence Production (economics) Training Point (geometry) ELETTRICI Training Computational modeling Current measurement Temperature measurement Predictive models Testing Computational intelligence Sustainable Development Maximum Power Point Temperature measurement business.industry Photovoltaic system Electrical engineering Control engineering Computational modeling Current measurement Solar Panels Power (physics) Solar Concentrators Planning business Energy (signal processing) Voltage |
| Zdroj: | I2MTC |
| Popis: | Salman, Ayşe (Dogus Author) -- Conference full title: 2014 IEEE International Instrumentation and Measurement Technology Conference: Instrumentation and Measurement for Sustainable Development, I2MTC 2014; Montevideo; Uruguay; 12 May 2014 through 15 May 2014 The power produced by a solar panel depends on several parameters. In order to optimize the production, the ability to operate in the Maximum Power Point (MPP) condition is requested. The ability to identify and reach the MPP condition is therefore critical to an efficient conversion of the photovoltaic energy. In this paper, several computational intelligence paradigms are challenged in the task of identifying the MPP power from the working condition directly measurable from the solar panel, such as the voltage, V, the current, I, and the temperature, T, of the panel. IEEE Instrumentation and Measurement Society (I and M) |
| Databáze: | OpenAIRE |
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