In this work, a novel control strategy is designed for integrating the hybrid distributed energy sources (DGs) into the DC microgrid system for enhancing the reliability of the system. The photovoltaic (PV) system is incorporated into the DC microgrid with super capacitor (SC) for improving the system performance at uncertainties in weather conditions. Further, adaptive neuro-fuzzy inference system is presented to regulate the voltage and sustain the power matching to load demand. Nevertheless, the accuracy and correctness of the system does not reach the optimal level due to low speed, takes more time for run. In this study, teaching learning-based optimization (TLBO) is utilized for tuning the adaptive neuro-fuzzy inference system (ANFIS) with the integration of DGs into DC microgrid system. In order to determine the effectiveness of the TLBO-based ANFIS, the system has been checked by diverse dynamic conditions like nominal state, changes in irradiance (1000–500 W/m2) 50% increment in AC load at 1.5 Sand 50% increment in DC load. As per simulation findings, the proposed TLBO-based ANFIS yields better enrichment in all considered dynamic conditions.


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    Titel :

    Control and Optimization of DC Microgrid Power Management with Energy Storage Devices and Photovoltaic System


    Beteiligte:
    Bharathi, G (Autor:in) / Kantarao, P (Autor:in) / Srinivasarao, R (Autor:in)


    Erscheinungsdatum :

    02.12.2021


    Format / Umfang :

    1757951 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch