用于配电网络故障线路选择的集成动态尖峰神经 P 系统

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Song Ma, Qiang Yang, Gexiang Zhang, Fan Li, Fan Yu, Xiu Yin
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引用次数: 0

摘要

由于中性点接地消弧线圈的补偿功能,配电网中的故障线路选择仍然面临挑战:经典模型在提取故障特征方面的学习能力不足,所使用的原始数据存在不平衡,导致故障线路选择的准确性较低。针对这一问题,本文提出了一种新型的尖峰神经 P(SNP)系统变体,即集成动态 SNP(IDSNP)系统,它由门控神经元、规则神经元和具有不同设计规则的权重神经元组成。此外,根据 IDSNP 系统,为配电网故障线路选择开发了 IDSNP(FL) 模型,其中用于传输加权神经元尖峰信息的层数可根据电力系统中电站数量的复杂性而动态改变。最后,在真实电力系统的实时调度数据集上对所提出的模型进行了评估。实验结果表明,IDSNP(FL) 模型与深度学习中的几个经典模型相比取得了最佳性能,验证了所提模型在配电网故障线路选择任务中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Integrated dynamic spiking neural P systems for fault line selection in distribution network

Integrated dynamic spiking neural P systems for fault line selection in distribution network

Due to the compensating function of neutral grounded arc suppression coil, fault line selection in distribution network is still facing challenges: the classical models have insufficient learning ability in extracting fault features, and there is an imbalance in the original data used, resulting in low accuracy in fault line selection. In order to address this issue, this paper proposes a novel variant of spiking neural P (SNP) systems called integrated dynamic SNP (IDSNP) systems, which consist of gated neurons, rule neurons, and weighed neurons with different designed rules. Furthermore, according to the IDSNP systems, an IDSNP(FL) model is developed for fault line selection in distribution network, where the number of layers for transmitting weighted neuron spiking information could be dynamically changeable depending on the complexity of the number of stations in the power system. Finally, the proposed model is evaluated on a real-time dispatch dataset of a real power system. The experimental results show that the IDSNP(FL) model achieves the best performance compared with several classical models in deep learning, verifying the effectiveness of the proposed model for fault line selection tasks in distribution network.

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来源期刊
Natural Computing
Natural Computing Computer Science-Computer Science Applications
CiteScore
4.40
自引率
4.80%
发文量
49
审稿时长
3 months
期刊介绍: The journal is soliciting papers on all aspects of natural computing. Because of the interdisciplinary character of the journal a special effort will be made to solicit survey, review, and tutorial papers which would make research trends in a given subarea more accessible to the broad audience of the journal.
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