Oscillatory Stability Monitoring and Control of the Power Grid With a High Penetration of Renewable Energy Sources - Perera, Pathirage Uvini Seneka (Auckland University of Technology, 2025)
Towards the sustainable development of the world, the traditional power system is rapidly shifting to using low carbon energy sources for power generation. This ongoing transformation however has detrimental effects on voltage stability, frequency stability, rotor angle stability, power quality, etc., and has resulted in many challenges on the stable operation of the power grid. Emerging sub synchronous oscillations (SSOs) in inverter-based resources (IBR) will be the focus of this thesis. Although extensive research has been conducted on SSO incidents related to synchronous generators, HVDC and flexible AC transmission systems (FACTS) in the past, there is a need for more research on SSOs in IBRs. The SSOs in IBRs such as wind or solar PVs can be primarily categorised as SSOs associated with DFIG-based (type 3) wind turbine generators connected to series compensated transmission lines and SSOs associated with DFIG-based/permanent magnet synchronous generator (PMSG)-based (type 4) wind turbine generators or solar PVs connected to weak AC grid. This thesis delves into SSOs in DFIG-based wind farms connected to series compensated transmission lines. It aims to formulate frameworks for monitoring and damping of SSOs in DFIG-based wind farms connected to series compensation. Continuous monitoring of the power system stability is essential for safe and reliable operation of the grid. Although existing wide area measurement systems (WAMS) can detect instabilities in general, it is inadequate to detect the sub synchronous oscillations. If the SSO conditions are not detected in due time, it can cause many undesirable effects such as damages to crowbar circuits, disconnection of large areas of wind farms from the grid, tripping of transmission lines, etc. Therefore, one of the goals of this thesis is to develop frameworks for accurate prediction of high risk SSOs in DFIG-based wind farms connected to series compensated transmission lines. Furthermore, the thesis also aims at designing a damping filter with an adaptive tuning strategy for mitigation of SSOs in DFIG-based wind farms connected to series compensation. The research work in this thesis can be discussed under three main sections. Firstly, this thesis presents an artificial intelligence-based method for predicting high risk SSOs in DFIG-based wind farms. It proposes to use machine learning models to predict severity of SSOs in DFIG-based wind farms. Four key parameters determining the severity of SSOs namely, wind speed, level of series compensation, no. of wind turbines in service and amplitude percentage of the strongest sub synchronous frequency component are used as the input parameters to the machine learning models. Several actions are proposed based on the model predictions to ensure the safety and reliability of the power grid. Next, this thesis presents a probabilistic study investigating the impact of the stochastic nature of wind and solar energy sources on SSOs in DFIG-based wind farms connected to series compensation and have PV installations nearby. The study examines the effects of the intermittency of wind speed and solar irradiation levels using TSHD. TSHD is used in sub harmonic protection relays to detect SSOs. Hence, this thesis provides an overview on how to adjust the relay settings to detect SSOs based on the stochastic behaviour of wind speed and solar irradiation. Lastly, this thesis proposes a filter-based damping mechanism to mitigate SSOs in DFIG-based wind farms connected to series compensation. To effectively damp SSOs in DFIG-based wind farms, it is proposed to dynamically tune the parameters of the damping filter. This tuning approach aims to adapt the filter characteristics to the operating conditions, ensuring consistent damping performance under varying operating conditions. Fluctuating wind speeds and number of in service wind turbines introduce significant variability to system operation. These variations can alter the dynamics of SSOs, making static filters less effective. By continuously adjusting the filter to respond to changing conditions, the system can maintain robust damping performance and ensure stability across a wide range of operating conditions. In conclusion, this research would benefit the modern power grid with a high penetration of renewable energy, as it addresses a critical issue related to the oscillatory stability of the power network.