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.

中文翻译：

---

在线电力系统安全评估的多元密度预测方法

本文设计了一种基于深度学习的多元密度预测模型，以预测电力系统中多个安全裕度的联合累积分布函数（JCDF）。与现有的多元密度预测模型不同，该方法不需要关于预测目标分布的先验假设。此外，基于神经网络的通用逼近能力，该方法的价值域已被证明包括所有连续的JCDF。预测的JCDF进一步用于计算确定性安全评估指标，以评估未来电力系统运行的安全级别。数值测试证明了该方法相对于当前的多元密度预测模型的优越性。