A Multivariate Density Forecast Approach for Online Power System Security Assessment

TL;DR

This paper introduces a deep learning-based multivariate density forecast model for power system security assessment that does not rely on prior distribution assumptions and demonstrates superior performance in numerical tests.

Contribution

The paper proposes a novel neural network-based multivariate density forecast method that includes all continuous JCDFs without prior distribution assumptions.

Findings

The proposed model outperforms existing multivariate density forecast models.

The security assessment index derived from JCDFs is more informative for operators.

Numerical tests confirm the method's effectiveness and accuracy.

Abstract

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.