A Multivariate Density Forecast Approach for Online Power System Security Assessment
This work addresses power system security assessment for grid operators, offering an incremental improvement by enhancing multivariate density forecasting methods.
The paper tackles the problem of forecasting joint cumulative distribution functions for power system security margins using a deep learning model without requiring prior distribution assumptions, and demonstrates its superiority over existing models while showing that the derived security assessment index provides more informative guidance for operators.
A multivariate density forecast model based on deep learning is designed in this paper to forecast the joint cumulative distribution functions (JCDFs) of multiple security margins in power systems. Differing from existing multivariate density forecast models, the proposed method requires no a priori hypotheses on the distribution of forecasting targets. In addition, based on the universal approximation capability of neural networks, the value domain of the proposed approach has been proven to include all continuous JCDFs. The forecasted JCDF is further employed to calculate the deterministic security assessment index evaluating the security level of future power system operations. Numerical tests verify the superiority of the proposed method over current multivariate density forecast models. The deterministic security assessment index is demonstrated to be more informative for operators than security margins as well.