Development of Wavelet and ANN-Based Algorithm in LabVIEW Environment for Classifying the Power Quality Disturbances
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0763-8_35
Reference7 articles.
1. Kankale RS, Paraskar SR, Jadhao SS (2022) Classification of power quality disturbances in emerging power system using discrete wavelet transform and K-nearest neighbor. ECS Trans 107:5281–5291. https://doi.org/10.1149/10701.5281ecst
2. Zhang P, Feng Q, Chen R, Wang D, Ren L (2020) Classification and identification of power quality in distribution network. In: 2020 5th international conference on power and renewable energy, ICPRE 2020, vol 4, pp 533–537. https://doi.org/10.1109/ICPRE51194.2020.92
3. Zaro FR, Abido MA (2019) Real-time detection and classification of power quality problems based on wavelet transform. JJEE 5(4):222–242
4. Hole SD, Naik CA (2020) Power quality events’ classification employing discrete wavelet transform and machine learning. In: 2020 1st IEEE international conference on measurement, instrumentation, control and automation, ICMICA 2020. https://doi.org/10.1109/ICMICA48462.2020.9242894
5. Pukhova VM, Ferrini G (2018) Time-frequency analysis of non-stationary signals, pp 1141–1145
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