Robustness and adaptability analysis for equivalent model of doubly fed induction generator wind farm using measured data

Abstract As many large wind farms connected to the power grid, it is necessary to develop a robust and adaptable dynamic equivalent model of the wind farm for system analysis and control. In this paper, the trajectory sensitivity of time-varying parameters of the equivalent model is analyzed. Then the non-time- varying parameters of the equivalent model are fixed as aggregated values, while the time-varying parameters are identified using the genetic learning particle swarm optimization based on phasor measurement unit data at the point of interconnection. The robustness and adaptability of the equivalent model under different scenarios are analyzed. The simulation results using the Western Electricity Coordinating Council benchmark test system show that the global searching capability to find the optimal point of the proposed method is higher than canonical particle swarm optimization and genetic algorithm by 2 orders. Further, the biggest mismatch between the identification results of the proposed method and the true values is within 10% for parameters with high sensitivity which is much better than previous work.