Open Access
Issue
ITM Web Conf.
Volume 45, 2022
2021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
Article Number 01028
Number of page(s) 11
Section Computer Technology and System Design
DOI https://doi.org/10.1051/itmconf/20224501028
Published online 19 May 2022
  1. W Fei, Dynasty, R Lin Tao. Summary of Research on Power Quality Disturbance Detection and Identification Method [J]. Proceedings of the Chinese Society for Electrical Engineering, 2021, 41 (12): 4104-4121. [Google Scholar]
  2. X Xiangning, L Kunyu, T Songhao, F Wenjie. New Development and Ultra Harmonic Problem of Electric Power Electronicization [J]. Journal of Electrician Technology, 2018, 33 (04): 707-720. [Google Scholar]
  3. R. Vinotha and K. K. Poongodi. Power Quality Improvement Using D-statcom[J]. International Journal of Innovative Research and Development, 2013, 2(4) : 176-185. [Google Scholar]
  4. PardoZamora Oscar N et al. Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization.[J]. Sensors (Basel, Switzerland), 2021, 21(11) [Google Scholar]
  5. C Yueming, F Xiyong, D Hongwei, L Mingxiang, D Xiaohua, Y Road. Method for perceived adaptive data processing for the edge node of power network [J]. Chiza Technology, 2019, 45 (06): 1715-1722 [Google Scholar]
  6. C Haoyong et al. Distributed sensing and cooperative estimation/detection of ubiquitous power internet of things [J]. Protection and Control of Modern Power Systems, 2019, 4(1): 1-8. [Google Scholar]
  7. Sindi Hatem et al. An adaptive deep learning framework to classify unknown composite power quality event using known single power quality events [J]. Expert Systems With Applications, 2021, 178 [Google Scholar]
  8. L Jinsong et al. Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network [J]. Frontiers in Energy Research, 2021 [Google Scholar]
  9. GonzalezAbreu ArtvinDarien et al. A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances [J]. Energies, 2021, 14(10): 2839-2839. [CrossRef] [Google Scholar]
  10. L Xuejun, G Jianhua, Z Lu, Z Zhennan, W Kai, Y Tiejiang. Electrical Quality Analysis of Optimization of Edge Calculation Task Allocation Optimization [J]. Electrical and Energy Efficiency Management Technology, 2021 (06): 92-98. [Google Scholar]
  11. Z Lijing, S Ge, J Xiuchen. Application Analysis and Research Prospect of Power Network in Substation in Electricity Network [J]. Hiqi Electric, 2020, 56 (09): 1-10. [Google Scholar]
  12. Khokhar, S., Zin, A. A. B. M., Mokhtar, et al. A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances [J]. Renewable and Sustainable Energy Reviews, 2015, 51(0):16501663. [CrossRef] [Google Scholar]
  13. Mahela O P, Shaik A G. Recognition of power quality disturbances using s-transform and fuzzy c-means clustering[C]//2016 International Conference on Cogeneration, Small Power Plants and District Energy (ICUE). IEEE, 2016: 1-6. [Google Scholar]
  14. Mahela O P, Sharma U K, Manglani T. Recognition of Power Quality Disturbances Using Discrete Wavelet Transform and Fuzzy C-means Clustering[C]//2018 IEEE 8th Power India International Conference (PIICON). IEEE, 2018: 1-6. [Google Scholar]
  15. Chakravorti T, Dash P K. Morphology based fuzzy approach for detection & classification of simultanious power quality disturbances[C]//2016 IEEE Annual India Conference (INDICON). IEEE, 2016: 1-6. [Google Scholar]
  16. Das D, Chakravorti T, Dash P K. Hilbert huang transform with fuzzy rules for feature selection and classification of power quality disturbances[C]//2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON). IEEE, 2017: 439-445. [Google Scholar]
  17. Rumelhart, David E., Geoffrey E. Hinton, et al. Learning representations by backpropagating errors. Nature, 1986, 323(0): 533-536. [CrossRef] [Google Scholar]
  18. Q Hezuo, L Xiaoming, C Chen, et al. Classification of power quality disturbances using convolutional neural network[J]. Engineering Journal of Wuhan University, 2018, 51(6): 534-539. [Google Scholar]
  19. C Wei, H Jiahuan, P Xiping. Application of Deep Belief Network in Power Quality Compound Disturbance Identification[J]. Proceedings of the CSU-EPSA, 2018, 30(9): 75-82 [Google Scholar]
  20. D Yuanhang, C Lei, Z Weiling, et al. Power System Transient Stability Assessment Based on Multi-Support Vector Machines [J]. Proceedings of the CSEE, 2016, 36(5): 1173-1180. [Google Scholar]
  21. X Zhichao, Y Lingjun, L Xiaoming. Power quality disturbance identification based on clustering-modified S-transform and direct support vector machine [J]. Electric Power Automation Equipment, 2015, 35(7): 50-58. [Google Scholar]
  22. Z Ting, Y Jun, Z Qiangming, et al. Power system transient stability assessment method based on modified LightGBM[J]. Power System Technology, 2019, 43(6): 1931-1940. [Google Scholar]
  23. IEEE Recommended Practice for Monitoring Electric Power Quality, IEEE Standard 1159-2019, Jun. 2019. [Google Scholar]
  24. H Ming, C Yu. Stranstective Detection and Positioning of Electrical Power Quality Based on Wavelet Transform Modularity [J]. Prural Technology, 2001 (03): 12-16. [Google Scholar]
  25. Hanif M, Dwivedi U D, Basu M, et al. Wavelet based islanding detection of DC-AC inverter interfaced DG systems[C]//UPEC2010.45th International Universities’ Power Engineering Conference. Cardiff: Cardiff University, 2010:1-5. [Google Scholar]

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