The advances of deep learning (DL) techniques bring new opportunities to numerous
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …

This paper concerns with the emerging power system stability issues, classification, and
research prospects under a high share of renewables and power electronics. The decades …

Due to infrastructure developments, wide disturbances have occurred in the power system.
There is a need for intelligent monitoring systems across wide power networks for the …

Power systems around the world are experiencing an energy revolution that substitutes
fossil fuels with renewable energy. Such a transition poses two significant challenges: highly …

Data-driven transient stability prediction (TSP) exists with issues of model robustness and
sample imbalance. An instance-based and parameter-based of hybrid transfer learning …

While many promising data-driven power system transient stability assessment (TSA)
studies have been recently reported, very few of them further propose efficient data-driven …

This work employs machine learning methods to develop and test a technique for dynamic
stability analysis of the mathematical model of a power system. A distinctive feature of the …

Transient stability assessment (TSA) has always been a fundamental means for ensuring
the secure and stable operation of power systems. Due to the integration of new elements …

Increased integration of renewable energy sources brings new challenges to the secure and
stable power system operation. Operational challenges emanating from the reduced system …

Data driven method based on big data and deep model is an effective tool to solve the
problem of online power system transient stability assessment. Nevertheless, when the …