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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的配电网故障定位

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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:本文研究了具有分布式发电(DG)的配电网的故障定位问题。提出了一种基于多阶段神经网络的故障识别与定位方法。采用粒子群算法(PSO)对网络结构进行优化。该模型和算法可以准确地实现故障定位。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,通过网络的逐步分解,其大小将大大减小。最后,在Digsilent环境中建立了具有分布式发电的典型配电网络。样本数据是通过模拟实验获得的。在Matlab中建立了神经网络模型以得到算法结果。实验证明了该模型和算法的正确性和有效性。

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