|Synchrophasor Data Event Detection using Unsupervised Wavelet Convolutional Autoencoders|
Timely and accurate detection of events affecting the stability and reliability of power transmission systems is crucial for safe grid operation. This paper presents an efficient unsupervised machine-learning algorithm for event detection using a combination of discrete wavelet transform (DWT) and convolutional autoencoders (CAE) with synchrophasor phasor measurements. These measurements are collected from a hardware-in-the-loop testbed setup equipped with a digital real-time simulator. Using DWT, the detail coefficients of measurements are obtained.
|Year of Publication||
2023 IEEE International Conference on Smart Computing (SMARTCOMP),
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