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Fault location in distribution network with distributed generation based on neural network

机译:基于神经网络的分布式发电中分发网络故障位置

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In this paper, the fault location problem of the distribution network with distributed generations(DGs) is studied. A fault identification and location method based on multi-stage neural network is proposed. Particle Swarm Optimization (PSO) algorithm is employed to optimize the network structure. The model and algorithm realize the fault location accurately. And when the input variables increases, When the input variables increases, its size is much reduced through gradual decomposition of the network compared to traditional the single-stage network. Finally, a typical distribution network with distributed generation is established in the Digsilent environment. The sample data are obtained through simulation experiment. The neural network model is established in Matlab to get the algorithm results. The experiment verified the correctness and effectiveness of the model and algorithm.
机译:在本文中,研究了分布式代代(DGS)的分发网络故障定位问题。提出了一种基于多级神经网络的故障识别和定位方法。粒子群优化(PSO)算法用于优化网络结构。该模型和算法精确地实现了故障位置。并且当输入变量增加时,当输入变量增加时,与传统的单级网络相比,通过网络的逐渐分解,其尺寸大大降低。最后,在Digsilent环境中建立了具有分布生成的典型分发网络。通过仿真实验获得样品数据。在MATLAB中建立神经网络模型以获得算法结果。实验验证了模型和算法的正确性和有效性。

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