A Combined Wavelet and Data-Mining Based Intelligent Protection Scheme for Microgrid
By D. Mishra, S. R. Samantaray, Geza Joos. IEEE Transactions on Smart Grid, 2016
322 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/1ec534eaed4e568a78794bd1ee56535d1091d490
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
In Electrical and Electronic Engineering
- Yunhe Hou
- James Jian Qiao Yu
- Victor O. K. Li
- Tao Huang
- Albert Y. S. Lam
- Ettore Francesco Bompard
- Yang Zhang
Citations in the corpus
- Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks (2019, methodology): “In addition, digital signal processing approaches such as discrete Fourier transform and discrete wavelet transform (DWT) are widely adopted to “pre-process” the input signals to better extract the time-frequency characteristics for analysis [3], [4], [12].”
- Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks (2018, methodology): “sequences are generally too long to be employed in subsequent calculations [36].”
- Big data analytics in smart grids: a review (2018, methodology): “The ability to operate in an island mode also protects the load from damages caused by power system including voltage fluctuation, frequency deviation, etc. (Mishra et al., 2016).”