Power converters and motor drives are playing a significant role in the transition towards
sustainable energy systems and transportation electrification. In this context, rich diversity of …

The integration of artificial intelligence (AI) techniques in power converter-based systems
has the potential to revolutionize the way these systems are optimized and controlled. With …

The widespread integration of inverter-based resources (IBRs) in modern power grids raises
concerns about low-frequency oscillations, impacting system stability and reliability globally …

In this letter, a hybrid method of fault detection using data and models, based on easy
knowledge transfer learning, is proposed. The proposed method is applied for multiple …

Due to the complexity of the actual operating conditions of lithium-ion batteries, accurately
estimating the state-of-health (SOH) of them often requires a significant amount of battery …

Data-driven approach is promising for predicting impedance profile of grid-connected
voltage source converters (VSCs) under a wide range of operating points (OPs). However …

Model predictive control (MPC) has been widely adopted to modular multilevel converter
(MMC)-based active rectifier due to its simplicity, flexibility, and ability to manage multiple …

The large-scale integration of renewables in the modern power system will lead to a large
number of power electronics in the power system and pose interaction stability challenges …

Accurate identification for the frequency-coupling admittance of the grid-connected
converters is a key factor in impedance stability analysis. However, not all information about …

This paper proposes a physics-informed deep transfer reinforcement learning (PIDTRL)
approach for power balance control and triple phase shift modulation method for the input …