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首页> 外文期刊>Renewable Power Generation, IET >Improved differential evolution-based Elman neural network controller for squirrel-cage induction generator system
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Improved differential evolution-based Elman neural network controller for squirrel-cage induction generator system

机译：改进的基于差分进化的鼠笼感应发电机系统的埃尔曼神经网络控制器

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摘要

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.
机译：提出了一种基于改进的差分进化（IDE）算法的埃尔曼神经网络（ENN）控制器，用于控制并网风力发电应用的鼠笼式感应发电机（SCIG）系统。首先，介绍了风力发电机仿真器的控制特性。然后，开发了AC / DC转换器和DC / AC逆变器，以将三相SCIG产生的电力转换为电网。此外，根据功率平衡原理，推导了SCIG系统的动态模型，用于控制直流母线电压的平方。此外，为了改善SCIG系统的直流母线电压平方的瞬态和稳态响应，提出了一种基于IDE的ENN控制器来控制SCIG系统。此外，还将详细介绍ENN的网络结构和在线学习算法。此外，根据不同的风速变化，还提供了由SCIG系统的动态模型使用IDE离线构建的查找表，用于优化ENN的学习率。最后，为了验证控制性能，提供了一些实验结果，以验证所提出的SCIG系统在并网风电应用中的可行性和有效性。

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