TR2021-087

Solving Bernoulli Bandit Problems for Weather-relative Overhead Distribution Line Failures Forecasting

    •  Wang, S., Sun, H., Kim, K.J., Guo, J., Nikovski, D.N., "Solving Bernoulli Bandit Problems for Weather-relative Overhead Distribution Line Failures Forecasting", IEEE PES GM, DOI: 10.1109/​PESGM46819.2021.9638153, July 2021.
      BibTeX TR2021-087 PDF
      • @inproceedings{Wang2021jul2,
      • author = {Wang, Shengyi and Sun, Hongbo and Kim, Kyeong Jin and Guo, Jianlin and Nikovski, Daniel N.},
      • title = {Solving Bernoulli Bandit Problems for Weather-relative Overhead Distribution Line Failures Forecasting},
      • booktitle = {IEEE PES GM},
      • year = 2021,
      • month = jul,
      • doi = {10.1109/PESGM46819.2021.9638153},
      • url = {https://www.merl.com/publications/TR2021-087}
      • }
  • MERL Contacts:
  • Research Area:

    Electric Systems

Abstract:

In this paper, an evaluation approach for analyzing the impact of weather events on line outage status in the distribution system is explored. By representing weather condition in a tabular form, the possible weather condition scenarios are reduced. Our failure forecasting problem is formulated as an online sequential decision-making problem. Each failure forecaster in the problem attempts to solve multiple independent Bernoulli Bandit problems. There are two learning frameworks based on maximum likelihood and maximum a posteriori used for designing a desired failure forecaster, which can online adjust model parameters using the reported outage data in order to mitigate the impact of unknown inherent uncertainties on model accuracy. A dataset with 10,000 artificial points is used to verify the effectiveness of two proposed algorithms. The results obtained by different action selection strategies are compared.