PhD Supervision

Abstract: As the penetration of renewable generation increases, the shortage of system strength has become a concern for maintaining stable power system operation. In most cases, Inverter-Based Resources (IBR) plants are operated using grid-following inverters (GFLI). Although GFLIs are prevalent in current inverter configurations, their growing integration into the grid can lead to significant stability challenges. In contrast, Grid-Forming Inverters (GFMIs) outperform GFLIs by providing capabilities such as standalone operation, frequency support, and enhanced adaptability in weak grid conditions. Addationally, GFMIs regulate both AC voltage and frequency at the Point of Common Coupling (PCC), improving the inverter's dynamic response to grid disturbances and enhancing overall system stability. Research has now shifted towards GFMIs, which mimic the behavior of synchronous generators. Therefore, accurate modeling and analysis are essential to fully understand the operation of grid-forming inverters. This work aims to develop a novel modeling and control approach based on deep neural network. The proposed model and control method will be developed and tested using Matlab. Furthermore, the optimal control method will be validated through hardware-in-the-loop (HIL) testing using Speedgoat, and the new controller for the GFMI will be applied to a weak grid (a segment of the Libyan grid) to assess the proposed control using PowerFactory software tools. The proposed control strategies include frequency-droop control, angle-droop control, Power Synchronization Control PSC, synchronverter, Virtual Synchrounous Machine (VSM) control, Virtual Oscillator Control (VOC), dispatchable Virtual Oscillator Control (dVOC), and Matching control. While GFMIs have been successfully implemented in countries like Australia, the US, and the UK, further technological advancements and research, especially in the application of Artificial Intelligent (AI) and machine learning for enhancing transient stability, are crucial for their broader adoption.