Wind farm model for power system stability analysis

Wind Power Modeling;Power System Simulations;Model Order Reduction

Pulgar Painemal, Hector A.

关键词: Wind Power Modeling;    Power System Simulations;    Model Order Reduction;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/18263/PulgarPainemal_Hector.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

In this thesis, the modeling of wind farms based on type-C wind power generators (WTGs) is studied. Based on time scale decomposition, two detailed dynamic models are presented. In both models, a rotor speed controller, a reactive power controller and a pitch angle controller are considered. The turbine's aerodynamic is represented by an static model and a single-mass model is assumed. With respect to the controllers, the speed controller is designed to extract maximum power from the wind for a given wind speed. The reactive power controller is designed to follow a reference. The pitch angle controller is designed to limit the maximum active power output. All controllers use proportional and integral control. Modal and bifurcation analysis is performed revealing that WTG's variables do not exhibit major oscillatory behavior when the system is perturbed. Moreover, WTG's variables do not participate in unstable modes and they do not change the system stability structure. In general, the most important interaction between WTGs and the system is the interchange of power. An aggregated model is proposed for wind farms. This model is characterized by a single equivalent WTG and an equivalent wind speed. Moreover, the order of the aggregated model is reduced by using selective modal analysis. This technique focusses on the most relevant modes and most relevant variables. Irrelevant variables are expressed in terms of the relevant ones which allows reducing the model order. Replacing either a two-axis or zero-axis model of a WTG for the reduced order model neither considerably alters the original system dynamics nor modifies the system variables. An important reduction of simulation time and model complexity is obtained. In the largest case, it is shown that two wind farms that in total are represented by 500 differential equations and 800 algebraic equations can be represented by just 4 differential equations.