Time–Frequency Convolution Neural Network for Classification of Single and Combined Power Quality Disturbances
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2550-2_57
Reference18 articles.
1. de Oliveira RA, Bollen MHJ (2023) Deep learning for power quality. Electr Power Syst Res 214:108887. https://doi.org/10.1016/j.epsr.2022.108887
2. IEC (2015) ‘61000-4-30: 2015’, Electromagnetic compatibility (EMC)— Part 4–30: Testing and measurement techniques—Power quality measurement methods, pp 1–85
3. IEEE recommended practice for monitoring electric power quality. IEEE Std 1159–2019 (Revision IEEE Std 1159–2009) 1–98 (2019). https://doi.org/10.1109/IEEESTD.2019.8796486
4. Vaghera P, Kumar D, Kothari N, Niamatullah S (2020) Identification of the source of power quality degradation using signature extraction from voltage waveforms. https://doi.org/10.1007/978-981-15-0206-4_19
5. Moyal D, Kothari N, Vaghera P, Kumar D (2021) Classifying power quality disturbance using time and multiresolution features through artificial neural network. In: 2021 International conference on intelligent technologies (CONIT). IEEE, pp 1–5. https://doi.org/10.1109/CONIT51480.2021.9498515
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