RT24 MIDDLE EAST | Enhancing Resiliency of a Low Inertia Power System (LIPS) Under Large-Scale Disturbances and Contingencies

Sub-synchronous oscillations (SSO) threaten the stability of power grids integrated with wind energy, especially in Type 4 wind farms operating under weak grid conditions, where fast dynamics of converter control can interact adversely with the grid. These oscillations can cause sustained disturbances in active power, PCC voltage, and output current, potentially leading to cascading system failures. Traditional methods for SSO detection, such as frequency scanning and impedance analysis, are complex and unsuitable for highly variable grid conditions. This talk introduces an intelligent machine learning-based approach for automated SSO detection and mitigation in Type 4 wind farms, validated through Hardware-In-the-Loop (HIL) experiments using OPAL-RT OP7000 and Raspberry Pi 5. The presentation explores SSO characteristics, the relationship between grid strength and WTG control dynamics, data generation via real-time simulation, and feature extraction for training an LSTM-based model. Case studies highlight the effectiveness of the proposed methodology in ensuring swift SSO detection and mitigation, emphasizing the utility of OPAL-RT's OP7000 in creating real-world replicas of offshore wind farms.