Refined Equivalent Modeling Method for Mixed Wind Farms Based on Small Sample Data

Abstract

For equivalent modeling of mixed wind farms (WFs), existing clustering indicators cannot consider the complex coupling characteristics between different types of wind turbines (WTs). In this paper, a refined equivalent modeling approach based on artificial intelligence technology is proposed. Firstly, the electromechanical transient performance of mixed WFs is analyzed. The WT type, wind speed and direction, and voltage dip are considered the dominant factors affecting the external dynamic response of mixed WFs. Secondly, the equivalent node model is established, including the selection of independent and dependent variables. Then, the multiple artificial neural networks (ANNs) are trained one by one based on small sample data, to fit the nonlinear relationship between the dependent variables and the independent variables. Finally, the dynamic response of the power systems with a mixed WF is simulated in the MATLAB platform. A comparison of the errors in electromechanical phenomena demonstrates that the proposed model can reflect the external characteristics of the test mixed WF in different wind conditions and voltage dips.

Maximum power optimization of a direct-drive wind turbine connected to PMSG using multi-objective genetic algorithm

“…Proportional integral, or PI for short, it a type in device employed in industrial settings to control numerous process factors. All the every process parameters in this controller are governed by a control loop feedback device to direct a structure toward an otherwise level of goal [21] location, use this type of command. In order to keep a technique's true output as near to the objective as is practical, this controller uses closed-loop feedback; if this is not possible, fixed-point output is employed; the transfer function c(s) provided by [22] (14); without a doubt, [23], [24] the process control industry's most widely utilized control algorithm is the PI controller.…”