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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    Title :

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


    Contributors:


    Publication date :

    2024-07-31


    Size :

    1124569 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English