A multilevel deep learning method for big data analysis and emergency management of power system

Abstract

The general focus of this study is to design a multilevel deep learning model that provides big data analytics and emergency management knowledge. A big data covariance analysis approach has been used to find multilevel representations of data based on prior knowledge from large scale power systems. For purpose of meeting requirements of incremental knowledge discovery, an adaptive regression algorithm is presented. Given the multilevel operating status and development trend of power system, the emergency management techniques are then proposed to produce intelligent decision making support. In this paper, a multilevel clustered hidden Markov model based global optimization approach is considered for power system emergency management problem, which is an extension of the conventional optimal power flow problem. The objective is defined to generate operation mode that minimizes multilevel cost while satisfying different constraints. To demonstrate the effectiveness of the presented approach, this paper carefully compared the discriminatory power of knowledge discovery models that utilize deep learning with dimensionality reduction based method and machine learning without dimensionality reduction based method. The experimental results showed that the proposed multilevel deep learning approach consistently outperformed the traditional machine learning method. The emergency management of large scale power system may also benefit from the modified hidden Markov model and global optimization.