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  • PES
    Members: Free
    IEEE Members: $10.00
    Non-members: $20.00
    Duration: 01:00:19
Panel Session 06 Aug 2020

With the increasing uncertainties, probabilistic analysis has gained significant importance in power industries. However, traditional probabilistic analysis, including reliability analysis, risk assessment, and stochastic optimization, is normally time consuming. The heavy computational burden has become a bottleneck of their practical applications.
Data-driven methods have the potential to overcome certain computational barriers in the probabilistic analysis. For example, data-driven methods can extract the key feature of the uncertainty set (injection uncertainty and topology uncertainty) to reduce the necessary computational effort; data-driven methods can directly project the output from the input based on historical data information. The possible applications of data-drive approach include fault identification, stability analysis, and reliability analysis under uncertainties.
In this panel, we will show the promising data-driven solutions for probabilistic analysis, which may promote the practical applications of probabilistic analysis in power industries. Potential challenges of data-driven probabilistic analysis in power industries will be discussed.

Zhifang Yang, Douglas Logan
Primary Committee:
Analytic Methods for Power Systems (AMPS)
Sponsor Committees:
Reliability and Risk Analysis

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