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An Adaptive Neuro-Fuzzy Inference System-Based Intelligent Grid-Connected Photovoltaic Power Generation

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Book cover Advances in Computational Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 988))

Abstract

This paper deals the artificial intelligence-based adaptive neuro-fuzzy inference system (ANFIS) for achievement of peak power from solar modules. This controller does not require the previous knowledge of database and has economical implementation as there is no additional sensor needed. The performance of ANFIS control is investigated by considering grid-integrated photovoltaic (PV) system. ANFIS supervisory control regulates the duty cycle of buck/boost converter under abrupt solar insolation. Simulated responses interpret that ANFIS has robust, rapid, and precise behavior under different operating conditions. Classical methods suffer more oscillating behavior with high settling period to obtain maximum PV power.

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References

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Birsa Institute of Technology (Trust), Ranchi, 835217, India

    Neeraj Priyadarshi & Amarjeet Kumar Sharma

  2. School of Computing & Information Technology, REVA University, Bangalore, 560064, India

    Farooque Azam

  3. Faculty of Engineering, Pacific University, Udaipur, 313003, India

    Monika Vardia

Authors
  1. Neeraj Priyadarshi
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  2. Farooque Azam
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  3. Amarjeet Kumar Sharma
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  4. Monika Vardia
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Corresponding author

Correspondence to Neeraj Priyadarshi .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

    Sudip Kumar Sahana

  2. Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

    Vandana Bhattacharjee

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Cite this paper

Priyadarshi, N., Azam, F., Sharma, A.K., Vardia, M. (2020). An Adaptive Neuro-Fuzzy Inference System-Based Intelligent Grid-Connected Photovoltaic Power Generation. In: Sahana, S., Bhattacharjee, V. (eds) Advances in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 988. Springer, Singapore. https://doi.org/10.1007/978-981-13-8222-2_1

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