In this research work, three practical correction methods are proposed to mitigate the impact
of defective input features in power system data measurement for machine learning (ML) …

With the help of neural network-based representation learning, significant progress has
been recently made in data-driven online dynamic stability assessment (DSA) of complex …

Accurate models of generation systems are critical for maintaining reliable and secure grid
operations. In this paper, a novel and systematic approach is proposed to identify and …

A large number of electric vehicles (EVs) are connected to the grid, increasing the risk of
power quality deterioration. Meanwhile, power quality disturbances (PQDs) directly affect EV …

Power system operators obtain information about an electrical grid's current condition using
available tools in control centers. These tools employ simple algorithms for data analysis …

The traditional learning based static voltage stability monitoring methods require manual
labeling of a large number of training samples. Getting these labeled training sets is …

Facing the difficulty of expensive and trivial data collection and annotation, how to make a
deep learning-based short-term voltage stability assessment (STVSA) model work well on a …

This paper proposes an innovative long-term voltage stability assessment (VSA) approach
tolerant to missing data based on hierarchical grouping convolutional neural network …

Machine learning is a promising method to solve the stability assessment problems of
modern complex power systems. The interpretability of machine learning models is …

A novel feature reduction method based on deep learning is proposed for power system
transient stability assessment (TSA) in this paper. First of all, an original feature set including …