RISS 검색 - 국내학술지논문

무료
기관 내 무료
유료

내보내기
내책장담기
한글로보기

정확도순

내림차순

내림차순

10개씩 출력

1
Multi Label Deep Learning classification approach for False Data Injection Attacks in Smart Grid

( Prasanna Srinivasan. V ),( Balasubadra. K ),( Saravanan. K ),( Arjun. V. S ),( Malarkodi. S ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6
- 원문보기 2
  ScienceON
  
  KISS
The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.
2
Development of daily gridded rainfall dataset over the Ganga, Brahmaputra and Meghna river basins

Prasanna, Venkatraman,Subere, Juvy,Das, Dwijendra K.,Govindarajan, Srinivasan,Yasunari, Tetsuzo John Wiley Sons, Ltd 2014 Meteorological applications Vol.21 No.2
- 원문보기
AbstractThe India Meteorological Department (IMD) gridded rainfall dataset, the 47 Bangladesh gauge rainfall observations and the Tropical Rainfall Measuring Mission (TRMM) 3B42V6 satellite data are used in the present analysis. The nearest neighbour interpolation scheme is used, wherein the interpolated values are computed from a weighted sum of observations. The Bangladesh daily gauge measured rainfall is interpolated into regular grids of 0.5° × 0.5° resolution every day from January 1988 to December 2007 and appended with the daily gridded dataset of the IMD over the Indian region. A similar resolution dataset of 0.5° × 0.5° for the TRMM‐3B42V6 data from January 1998 to December 2007 is created from the original data of 0.25° × 0.25° resolution. To produce a merged rainfall product, all the gridded datasets are merged. The merging of datasets is done in such a way as to include the highest rainfall at each grid point from the three products. Based on the three available sets of daily observations (IMD dataset (1° × 1°), TRMM‐3B42 (0.25° × 0.25°) and 46 daily station observations over Bangladesh), a dataset of 0.5° × 0.5° resolution on a daily scale is generated. The focus of this study is to compare the TRMM‐3B42V6 rainfall data over the Ganga, Brahmaputra and Meghna (GBM) domain with observed point gauge data, and assess the possibility of using them for application in real time flood forecasting as well as to serve as a comparison tool for the baseline simulation of high resolution atmospheric models aimed at flood forecasting and climate change projections. Copyright © 2012 Royal Meteorological Society