• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
M. R. Zaidan, 2019, Power system fault detection, classification and clearance by artificial neural network controller, 2019 Global Conference for Advancement in Technology (GCAT), IEEE, pp. 1-5DOI
2 
S. Yoon, J. F. MacGregor, 2001, Fault diagnosis with multivariate statistical models part I: using steady state fault signatures, Journal of process control, Vol. 11, No. 4, pp. 387-400DOI
3 
H. Jiang, J. J. Zhang, W. Gao, Z. Wu, 2014, Fault detection, identification, and location in smart grid based on data-driven computational methods, IEEE Transactions on Smart Grid, Vol. 5, No. 6, pp. 2947-2956DOI
4 
S. A. Aleem, N. Shahid, I. H. Naqvi, 2015, Methodologies in power systems fault detection and diagnosis, Energy Systems, Vol. 6, pp. 85-108DOI
5 
V. Veerasamy, 2021, LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system, IEEE Access, Vol. 9, pp. 32672-32687DOI
6 
L. Cai, N. F. Thornhill, S. Kuenzel, B. C. Pal, 2017, Real- time detection of power system disturbances based on $ K $-nearest neighbor analysis, IEEE Access, Vol. 5, pp. 5631-5639DOI
7 
J. Cordova, C. Soto, M. Gilanifar, Y. Zhou, A. Srivastava, R. Arghandeh, 2018, Shape preserving incremental learning for power systems fault detection, IEEE control systems letters, Vol. 3, No. 1, pp. 85-90DOI
8 
A. F. Bastos, S. Santoso, 2019, Universal waveshape-based disturbance detection in power quality data using similarity metrics, IEEE Transactions on Power Delivery, Vol. 35, No. 4, pp. 1779-1787DOI
9 
V. Psaras, A. Emhemed, G. Adam, G. Burt, 2018, Review and evaluation of the state of the art of DC fault detection for HVDC grids, 2018 53rd International Universities Power Engineering Conference (UPEC), IEEE, pp. 1-6DOI
10 
M. Ramesh, A. J. Laxmi, 2012, Fault identification in HVDC using artificial intelligence—Recent trends and perspective, 2012 International Conference on Power, Signals, Controls and Computation, IEEE, pp. 1-6DOI
11 
Y. M. Yeap, N. Geddada, A. Ukil, 2017, Analysis and validation of wavelet transform based DC fault detection in HVDC system, Applied Soft Computing, Vol. 61, pp. 17-29DOI
12 
D. Ye, F. Xie, Z. Hao, 2021, A novel identification scheme of lightning disturbance in HVDC transmission lines based on CEEMD-HHT, CPSS Transactions on Power Electronics and Applications, Vol. 6, No. 2, pp. 145-154DOI
13 
S. Ghashghaei, M. Akhbari, 2021, Fault detection and classification of an HVDC transmission line using a heterogenous multi‐machine learning algorithm, IET Generation, Transmission & Distribution, Vol. 15, No. 16, pp. 2319-2332DOI
14 
T. Goswami, U. B. Roy, 2019, Predictive model for classification of power system faults using machine learning, TENCON 2019-2019 IEEE Region 10 Conference (TENCON), IEEE, pp. 1881-1885DOI
15 
Y. Wang, X. Wang, Y. Wu, Y. Guo, 2020, Power system fault classification and prediction based on a three-layer data mining structure, IEEE Access, Vol. 8, pp. 200897-200914DOI
16 
Y. Chen, Z. Lin, X. Zhao, G. Wang, Y. Gu, 2014, Deep learning-based classification of hyperspectral data, IEEE Journal of Selected topics in applied earth observations and remote sensing, Vol. 7, No. 6, pp. 2094-2107DOI
17 
Y.-Y. Zheng, J.-L. Kong, X.-B. Jin, X.-Y. Wang, T.-L. Su, M. Zuo, 2019, CropDeep: The crop vision dataset for deep-learning-based classification and detection in precision agriculture, Sensors, Vol. 19, No. 5, pp. 1058-DOI
18 
Priyanka, D. Kumar, 2020, Decision tree classifier: a detailed survey, International Journal of Information and Decision Sciences, Vol. 12, No. 3, pp. 246-269DOI
19 
A. M. Prasad, L. R. Iverson, A. Liaw, 2006, Newer classification and regression tree techniques: bagging and random forests for ecological prediction, Ecosystems, Vol. 9, pp. 181-199DOI
20 
S. Karamizadeh, S. M. Abdullah, M. Halimi, J. Shayan, M. javad Rajabi, 2014, Advantage and drawback of support vector machine functionality, 2014 international conference on computer, communications, and control technology (I4CT), IEEE, pp. 63-65DOI
21 
J. V. Tu, 1996, Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes, Journal of clinical epidemiology, Vol. 49, No. 11, pp. 1225-1231DOI
22 
A. Bilski, 2011, A review of artificial intelligence algorithms in document classification, International Journal of Electronics and Telecommunications, pp. -DOI
23 
Y. Huang, L. Li, 2011, Naive Bayes classification algorithm based on small sample set, 2011 IEEE International conference on cloud computing and intelligence systems, IEEE, pp. 34-39DOI
24 
S. Asante-Okyere, C. Shen, Y. Y. Ziggah, M. M. Rulegeya, X. Zhu, 2020, A novel hybrid technique of integrating gradient-boosted machine and clustering algorithms for lithology classification, Natural Resources Research, Vol. 29, pp. 2257-2273DOI