Aiming at the noise that may exist in the data collection and transmission of power system
synchronous phasor measurement unit (PMU), as well as the imbalance between stable and …

Increasing interconnections to fulfill the rising electrical demand and wide application of
power electronics devices has led to an increase in transient stability criterion. This leads to …

With the increasing complexity of power systems, the existing transient stability assessment
methods have many shortcomings in using power grid interconnection dynamic information …

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 …

This study proposes a model for transient stability assessment, which is a convolutional
neural network model combined with a saliency map (S–CNN model). The convolutional …

When severe disturbance occurs in power system, lack of efficacious information about
transient stability state is a key challenge for power network operator. Especially for the …

In the face of huge challenges in security and stability analysis brought by the growing scale
and complexity of modern power systems, the application of deep learning to the transient …

Although deep learning-based predictors have achieved high accuracy in phasor
measurement units (PMUs)-based post-fault transient stability assessment (TSA), most of …

In order to accurately evaluate power system stability in a timely manner after faults, and
further improve the feature extraction ability of the model, this paper presents an improved …

This paper puts forward a Bi-LSTM attention mechanism model based on voltage phasor for
electric power system transient stability assessment. The Bi-LSTM attention mechanism is …