The increasing integration of distributed generation systems into the grid to fulfil energy needs highlights the importance of addressing islanding cases, which pose a significant threat to grid stability. This paper focuses on developing a method for identifying islanding incidents to detect grid irregularities effectively. The methodology includes generating datasets that correspond to various grid anomalies and then utilizing a machine learning classifier for training. To support this process, a 16 kW $3 - \phi$ grid-tied photovoltaic (PV) system is modelled and operated in a Typhoon hardware-in-loop (HIL) setting to collect diverse datasets representing different grid anomaly scenarios. Following this, the data is utilized to train a k-nearest neighbor (kNN) classifier to establish the islanding detection system. The trained classifier demonstrates an accuracy of 98.58% and has the ability to classify at a rate of around 1400 observations per second.


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    Titel :

    Enhanced Islanding Detection for Grid-Connected PV Systems: A Machine Learning Approach


    Beteiligte:
    Anwar, Md Afghan (Autor:in) / Haque, Ahteshamul (Autor:in) / Khan, Md Zafar (Autor:in) / Mateen, Suwaiba (Autor:in)


    Erscheinungsdatum :

    31.07.2024


    Format / Umfang :

    1124569 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch