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首页> 外文期刊>Energy >Recurrent wavelet-based Elman neural network with modified gravitational search algorithm control for integrated offshore wind and wave power generation systems
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Recurrent wavelet-based Elman neural network with modified gravitational search algorithm control for integrated offshore wind and wave power generation systems

机译：基于递归小波的Elman神经网络及改进的重力搜索算法控制，用于海上风电和风电一体化发电系统

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A new approach to rotational speed control structures based on an optimized intelligent recurrent wavelet-based Elman neural network (RWENN) controller used for the integration of offshore wind and wave energy conversion systems driven by a doubly fed induction generator. The nodes connecting the weights of the RWENN are trained online using a backpropagation method. A modified gravitational search algorithm (MGSA) is developed to adjust the learning rates and improve learning capability. The proposed control scheme has improved the real power regulation and dynamic performance of a combined wind and ocean wave energy scheme over a wide range of operating conditions. The performance of this control scheme is assessed by comparing it to a traditional proportional-integral based control scheme in a series of case studies representative of maximum power generation. Simulations are carried out using PSCAD/EMTDC software to verify the robustness of the power electronics converters and the efficiency of the proposed controller under steady state and transient conditions. (C) 2018 Elsevier Ltd. All rights reserved.
机译：一种基于优化的基于智能小波的Elman神经网络（RWENN）控制器的转速控制结构的新方法，该控制器用于由双馈感应发电机驱动的海上风能和波浪能转换系统的集成。使用反向传播方法在线连接连接RWENN权重的节点。开发了一种改进的重力搜索算法（MGSA），以调节学习速率并提高学习能力。所提出的控制方案在广泛的工作条件下提高了风电和海浪能源组合能源计划的有功功率调节和动态性能。在一系列代表最大发电量的案例研究中，通过将其与传统的基于比例积分的控制方案进行比较来评估该控制方案的性能。使用PSCAD / EMTDC软件进行了仿真，以验证电力电子转换器的稳健性以及所提出的控制器在稳态和瞬态条件下的效率。 （C）2018 Elsevier Ltd.保留所有权利。

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