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Fuzzy Cognitive Maps pp 231–257Cite as

Fuzzy Cognitive Networks for Maximum Power Point Tracking in Photovoltaic Arrays

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 247))

Abstract

The studies on the photovoltaic (PV) power generation are extensively increasing, since it is considered as an essentially inexhaustible and broadly available energy resource. However, the output power of the photovoltaic modules depends on solar radiation and temperature of the solar cells. Therefore, to maximize the efficiency of the renewable energy system, it is necessary to track the maximum power point of the PV array and make the array operate near it. Maximum power operation is a challenging problem, since it requires that the system load is capable of using all power available from the PV system at all times. The I-V characteristic of the load must intersect the focus of maximum power points on the I-V characteristics of the PV array for varying insolation and temperature levels. Fuzzy Cognitive Networks (FCN) have been proposed as an operational extension of Fuzzy Cognitive Maps (FCM), which work in continuous interaction with the system they describe and may be used to control it. In this chapter FCN is used to construct a maximum power point tracker (MPPT), which may operate in cooperation with a fuzzy MPPT controller. The proposed scheme outperforms other existing MPPT schemes of the literature giving very good maximum power operation of any PV array under different conditions such as changing insolation and temperature. Moreover it has the ability to adapt to different changes that might happen during the life cycle of the PV module, such as a destroyed cell of the PV array.

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Author information

Authors and Affiliations

  1. Automatic Control Systems Lab., Dept. of Electrical & Computer Engineering, Democritus University of Thrace, V. Sofias 12, 67100, Xanthi, Greece

    Thodoris L. Kottas & Yiannis S. Boutalis

  2. Electrical Machines Lab., Dept. of Electrical & Computer Engineering, Democritus University of Thrace, V. Sofias 12, 67100, Xanthi, Greece

    Athanasios D. Karlis

Authors
  1. Thodoris L. Kottas
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  2. Athanasios D. Karlis
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  3. Yiannis S. Boutalis
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Editors and Affiliations

  1. University of Aegean, Chios, Greece

    Michael Glykas

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Kottas, T.L., Karlis, A.D., Boutalis, Y.S. (2010). Fuzzy Cognitive Networks for Maximum Power Point Tracking in Photovoltaic Arrays. In: Glykas, M. (eds) Fuzzy Cognitive Maps. Studies in Fuzziness and Soft Computing, vol 247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03220-2_10

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  • DOI: https://doi.org/10.1007/978-3-642-03220-2_10

  • Publisher Name: Springer, Berlin, Heidelberg

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  • Online ISBN: 978-3-642-03220-2

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