基于神经网络的分布式发电机配电系统故障定位方案

Q4 Engineering
Shahgholian Ghazanfar, M. Rezaei
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引用次数: 1

摘要

DG (distributed generation)由于其诸多优点,在各种情况下得到了广泛的应用,但DG与径向配电系统的互连会对保护装置的协调产生一定的影响。故障定位诊断是保护方案的重点,因此有必要研究一种新的、高精度的故障定位识别方法。本文提出了一种基于神经网络的分布式系统故障定位识别方法。根据该方法,在必要条件下使用分布式系统作为故意孤岛是可能的,并减少了电网的ENS(能源不供应)。为每个岛(区域)使用单独的nn(神经网络)将提高该方法的准确性。并对该方案在实际分布式系统上的实现结果进行了仿真和报告。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FAULT LOCATION SCHEME IN DISTRIBUTION SYSTEMS WITH DISTRIBUTED GENERATORS USING NEURAL NETWORKS
Nowadays using DG (distributed generation) in vast variety of cases has been more considerable due to its beneficial advantages, but interconnecting DG to radial distribution systems has some impact on the coordination of protection devices. The main point in the protection scheme is the diagnosis of fault locations, so producing a new method to identify fault location with high accuracy is necessary. This paper presents a novel approach to fault location identification with DG in distributed systems by the means of neural networks. According to this method using a distributed system as intentional islanding in necessary conditions is possible and reduces the ENS (Energy Not Supplied) of the net. Using separate NNs (neural networks) for each island (zone) will increase the accuracy of this method. Implementation results of this scheme on actual distributed systems has been simulated and reported.
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来源期刊
Majlesi Journal of Electrical Engineering
Majlesi Journal of Electrical Engineering Engineering-Electrical and Electronic Engineering
CiteScore
1.20
自引率
0.00%
发文量
9
期刊介绍: The scope of Majlesi Journal of Electrcial Engineering (MJEE) is ranging from mathematical foundation to practical engineering design in all areas of electrical engineering. The editorial board is international and original unpublished papers are welcome from throughout the world. The journal is devoted primarily to research papers, but very high quality survey and tutorial papers are also published. There is no publication charge for the authors.
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