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Improved differential evolution-based Elman neural network controller for squirrel-cage induction generator system

Improved differential evolution-based Elman neural network controller for squirrel-cage induction generator system

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An improved differential evolution (IDE) algorithm-based Elman neural network (ENN) controller is proposed to control a squirrel-cage induction generator (SCIG) system for grid-connected wind power applications. First, the control characteristics of a wind turbine emulator are introduced. Then, an AC/DC converter and a DC/AC inverter are developed to convert the electric power generated by a three-phase SCIG to the grid. Moreover, the dynamic model of the SCIG system is derived for the control of the square of DC-link voltage according to the principle of power balance. Furthermore, in order to improve the transient and steady-state responses of the square of DC-link voltage of the SCIG system, an IDE-based ENN controller is proposed for the control of the SCIG system. In addition, the network structure and the online learning algorithm of the ENN are described in detail. Additionally, according to the different wind speed variations, a lookup table built offline by the dynamic model of the SCIG system using the IDE is provided for the optimisation of the learning rates of ENN. Finally, to verify the control performance, some experimental results are provided to verify the feasibility and the effectiveness of the proposed SCIG system for grid-connected wind power applications.

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