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Enhancing Power Quality Event Classification with AI Transformer Models
It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99. 81%$-$91. 43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency.
Learning-Based Detection of Malicious Volt-VAr Control Parameters in Smart Inverters
In contrast with previous works that proposed methods to mitigate the oscillations after they are already present in the system, this paper presents an intrusion detection method to detect malicious VVC curves once they are communicated to the inverter.
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