Multi-objective Fuzzy-optimization of Crowbar Resistances for the Low-Voltage Ride-through of Doubly Fed Induction Wind Turbine Generation Systems

This study investigates the multi-objective fuzzy optimization of crowbar resistance for the doubly fed induction generator (DFIG) low-voltage ride-through (LVRT). By integrating the crowbar resistance of the crowbar circuit as a decision variable, a multi-objective model for crowbar resistance value optimization has been established to minimize rotor overcurrent and to simultaneously reduce the DFIG reactive power absorbed from the grid during the process of LVRT. A multi-objective genetic algorithm (MOGA) is applied to solve this optimization problem. In the proposed GA, the value of the crowbar resistance is represented by floating-point numbers in the GA population. The MOGA emphasizes the non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy-set-theory-based is employed to obtain the best solution. The proposed approach has been evaluated on a 3 MW DFIG LVRT. Simulation results show the effectiveness of the proposed approach for solving the crowbar resistance multi-objective optimization problem in the DFIG LVRT.

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Cite this article

[IEEE Style]

W. Zhang, H. Ma, J. Zhang, L. Chen, Y. Qu, "Multi-objective Fuzzy-optimization of Crowbar Resistances for the Low-Voltage Ride-through of Doubly Fed Induction Wind Turbine Generation Systems," Journal of Power Electronics, vol. 15, no. 4, pp. 1119-1130, 2015. DOI: 10.6113/JPE.2015.15.4.1119.

[ACM Style]

Wenjuan Zhang, Haomiao Ma, Junli Zhang, Lingling Chen, and Yang Qu. 2015. Multi-objective Fuzzy-optimization of Crowbar Resistances for the Low-Voltage Ride-through of Doubly Fed Induction Wind Turbine Generation Systems. Journal of Power Electronics, 15, 4, (2015), 1119-1130. DOI: 10.6113/JPE.2015.15.4.1119.