• Corpus ID: 195345788

An Unsupervised Learning Method for Early Event Detection in Smart Grid with Big Data

@article{He2015AnUL,
  title={An Unsupervised Learning Method for Early Event Detection in Smart Grid with Big Data},
  author={Xing He and Robert Caiming Qiu and Qian Ai and Xinyi Xu},
  journal={ArXiv},
  year={2015},
  volume={abs/1502.00060}
}

• Xing He, R. Qiu, +1 author Xinyi Xu
• Published 30 January 2015
• Computer Science, Engineering
• ArXiv

Early Event Detection (EED) is becoming increasingly complicated in smart grids, due to the exploration of data with features of volume, velocity, variety, and veracity (i.e. 4Vs data). This paper develops a data-driven unsupervised learning method based on random matrix theory (RMT) to handle this challenge problem. Compared to model-based methods, datadriven ones conduct data analysis requiring no knowledge of the system model/topology based on assumptions or simplifications. On the other hand, compared to supervised learning methods, unsupervised ones extract analysis directly from the raw data without any label. The proposed method using to processing all the raw data rather than only the labeled ones, calculates the mean spectral energy radius (MSR) as high-dimensional statistic and visualize it to form 3D power-map for EED. This method is easier in logic, faster in speed, and more universal and objective. Moreover, it is veracious and robust to traditional EED challenges such as error accumulations, spurious correlations, incidental correlations, and even bad data existence in the core area. Case studies with both simulated data and realistic data validate the effectiveness and higher performance of the proposed method for EED execution in smart grids.