PMSG-based wind power integration-modelling and analysis of impacts on the dynamic performances of a power system and mitigation under stochastic wind disturbances.

Abstract

Because of the ever-growing demand for electrical energy and environmental challenges of fossil fuel consumption, a priority has been given to the development of wind energy systems, among which, currently, permanent magnet synchronous generator (PMSG)-based wind power is receiving much attention from researchers, engineers, and turbine manufacturers. However, high PMSG-based wind power integration into a power system brings several challenges to transmission system operators. One of the challenges is its impacts on the dynamic performances of a power system due to the presence of stochastic wind disturbances. Thus, for a thorough investigation of the influences of stochastic wind speed disturbances, a proper wind speed model should be adopted. Therefore, this thesis proposes the use of Markov chain model for modelling wind speed series in dynamic simulations of wind turbines. In this regard, comparison of statistical quantities of measured wind speed data from Durban and Markov model generated ones confirms the accuracy of the model adopted. The results have shown that the dynamic performances of a power system deteriorate with the presence of stochastic wind speed disturbances, and thus the need for improving poor dynamic performances. Wind gusts cause stress, over currents, over voltages and instability in a power system. This thesis, therefore, introduces novel mitigation techniques based on virtual controls stemming from real resistors, compensators, and damper windings, and supplementary controllers to enhance the dynamic performances of a wind turbine directdriven PMSG, the main component of a PMSG-based wind farm. In the proposed schemes, the virtual controllers adjust the terminal d- and q-axis reference voltages in the generator side converter controller and their influences on the dynamic performances of the wind turbine are investigated. MATLAB/Simulink simulations on a wind turbine connected to an infinite bus show that virtual controls are effective in enhancing the dynamic performances of the PMSG. Local oscillations caused by wind disturbances are efficiently suppressed. Overall, the proposed mitigation techniques smooth the rotor speed and power of a PMSG, and hence reducing the influences of the stochastic wind speed disturbances. Furthermore, the results have demonstrated that stochastic wind speed disturbances affect the dynamic performances of a power system containing a PMSG-based wind farm as the dynamics of synchronous machines within the system depend on power balance, which is influenced by the power response of the wind farm. Finally, investigations in this thesis have confirmed that virtual controls and FACTS devices such as STATCOM and SVC are efficient in improving the dynamic performances of a power system containing PMSG-based wind farms under stochastic wind disturbances.