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All issues Volume 377 (2023) MATEC Web Conf., 377 (2023) 01021 References
Open Access
Issue
MATEC Web Conf.
Volume 377, 2023
Curtin Global Campus Higher Degree by Research Colloquium (CGCHDRC 2022)
Article Number 01021
Number of page(s) 7
Section Engineering and Technologies for Sustainable Development
DOI https://doi.org/10.1051/matecconf/202337701021
Published online 17 April 2023
  1. P.K. Maroti, S. Padmanaban, M.S. Bhaskar, V.K. Ramachandaramurthy, F. Blaabjerg, The state-of-the-art of power electronics converters configurations in electric vehicle technologies, Power Electronic Devices and Components p. 100001 (2021) [Google Scholar]
  2. M.R. Khalid, I.A. Khan, S. Hameed, M.S.J. Asghar, J.S. Ro, A comprehensive review on structural topologies, power levels, energy storage systems, and standards for electric vehicle charging stations and their impacts on grid, IEEE Access 9, 128069 (2021) [CrossRef] [Google Scholar]
  3. L. Wang, Z. Qin, T. Slangen, P. Bauer, T. van Wijk, Grid impact of electric vehicle fast charging stations: Trends, standards, issues and mitigation measures-an overview, IEEE Open Journal of Power Electronics 2, 56 (2021) [CrossRef] [Google Scholar]
  4. E. Elbouchikhi, M.F. Zia, M. Benbouzid, S. El Hani, Overview of signal processing and machine learning for smart grid condition monitoring, Electronics 10, 2725 (2021) [CrossRef] [Google Scholar]
  5. A. Ghasempour, Internet of things in smart grid: Architecture, applications, services, key technologies, and challenges, Inventions 4, 22 (2019) [CrossRef] [Google Scholar]
  6. A. Jain, S. Bhullar, Online management and assessment of power quality issues through smart metering in smart grid architecture, Energy Reports 8, 613 (2022) [CrossRef] [Google Scholar]
  7. E.A. Nagata, D.D. Ferreira, C.A. Duque, A.S. Cequeira, Voltage sag and swell detection and segmentation based on Independent Component Analysis, Electric Power Systems Research 155, 274 (2018) [CrossRef] [Google Scholar]
  8. R. Mahla, B. Khan, O.P. Mahela, A. Singh, Recognition of complex and multiple power quality disturbances using wavelet packet-based fast kurtogram and ruled decision tree algorithm, International Journal of Modeling, Simulation, and Scientific Computing 12, 2150032 (2021) [CrossRef] [Google Scholar]
  9. Y. Liu, T. Jin, M.A. Mohamed, Q. Wang, A novel three-step classification approach based on time-dependent spectral features for complex power quality disturbances, IEEE Transactions on Instrumentation and Measurement 70, 1 (2021) [Google Scholar]
  10. S.Z. Motlagh, A.A. Foroud, Power quality disturbances recognition using adaptive chirp mode pursuit and grasshopper optimized support vector machines, Measurement 168, 108461 (2021) [CrossRef] [Google Scholar]
  11. S. Suganthi, A. Vinayagam, V. Veerasamy, A. Deepa, M. Abouhawwash, M. Thiru-Meni, Detection and classification of multiple power quality disturbances in Microgrid network using probabilistic based intelligent classifier, Sustainable Energy Technologies and Assessments 47, 101470 (2021) [CrossRef] [Google Scholar]
  12. C.I. Garcia, F. Grasso, A. Luchetta, M.C. Piccirilli, L. Paolucci, G. Talluri, A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM, Applied Sciences 10, 6755 (2020) [CrossRef] [Google Scholar]
  13. E. Balouji, I.Y. Gu, M.H. Bollen, A. Bagheri, M. Nazari, A LSTM-based deep learning method with application to voltage dip classification, in 2018 18th International Conference on Harmonics and Quality of Power (ICHQP) (IEEE, 2018), pp. 1–5 [Google Scholar]
  14. Y. Deng, H. Jia, P. Li, X. Tong, F. Li, A Deep Learning Method based on Long Short Term Memory and Sliding Time Window for Type Recognition and Time Location of Power Quality Disturbance, in 2018 Chinese Automation Congress (CAC) (IEEE, 2018), pp. 1764–1768 [CrossRef] [Google Scholar]
  15. S. Wang, H. Chen, A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network, Applied energy 235, 1126 (2019) [CrossRef] [Google Scholar]
  16. R. Machlev, A. Chachkes, J. Belikov, Y. Beck, Y. Levron, Open source dataset generator for power quality disturbances with deep-learning reference classifiers, Electric Power Systems Research 195, 107152 (2021) [CrossRef] [Google Scholar]
  17. M. Uyar, S. Yildirim, M.T. Gencoglu, An effective wavelet-based feature extraction method for classification of power quality disturbance signals, Electric power systems Research 78, 1747 (2008) [CrossRef] [Google Scholar]
  18. S. Avdakovic, A. Nuhanovic, M. Kusljugic, E. Becirovic, Applications of wavelets and neural networks for classification of power system dynamics events, Turkish Journal of Electrical Engineering and Computer Sciences 22, 327 (2014) [CrossRef] [Google Scholar]
  19. S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural computation 9, 1735 (1997) [CrossRef] [Google Scholar]
  20. S. Khokhar, A.A.M. Zin, A.P. Memon, A.S. Mokhtar, A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network, Measurement 95, 246 (2017) [CrossRef] [Google Scholar]
  21. B. Panigrahi, V.R. Pandi, Optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy k-nearest neighbour algorithm, IET generation, transmission & distribution 3, 296 (2009) [CrossRef] [Google Scholar]
  22. Q. Tang, W. Qiu, Y. Zhou, Classification of complex power quality disturbances using optimized S-transform and kernel SVM, IEEE Transactions on Industrial Electronics 67, 9715 (2019) [Google Scholar]

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