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Artificial neural network-based maximum power point tracking controller for real-time hybrid renewable energy system

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Abstract

Development of various maximum power point tracking (MPPT) control techniques for proposed systems such as solar photo-voltaic (PV), wind turbine (WT), fuel cell (FC) and hybrid renewable energy system (HRES). HRES is the combination of PV, WT and FC which is connected parallelly by DC link. It is implemented in real-time using OPAL-RT system. In this research article, the MPPT algorithms viz. Perturb and Observe (P&O), Fuzzy Logic (FL), Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been analyzed and compared. Results have been carried out to record tracking performance of MPPT controllers by introducing changes in the radiation, wind speed hydrogen fuel rate. It has been observed that the proposed HRES using ANFIS-based MPPT controller provides better response as compared to other specified MPPT controllers.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Mewat Engineering College, Nuh, Haryana, India

    Mohammad Junaid Khan

  2. Department of Electrical Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, 160019, India

    Lini Mathew

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  1. Mohammad Junaid Khan
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  2. Lini Mathew
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Correspondence to Mohammad Junaid Khan.

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Khan, M.J., Mathew, L. Artificial neural network-based maximum power point tracking controller for real-time hybrid renewable energy system. Soft Comput 25, 6557–6575 (2021). https://doi.org/10.1007/s00500-021-05653-0

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