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Reliability Evaluation of Distribution Power Systems Based on Artificial Neural Network Techniques

机译:基于人工神经网络技术的配电系统可靠性评估

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摘要

In order to assess the reliability of distribution systems, more and more researchers are directing their attention to the artificial intelligent method, and several reliability indices have been proposed, such as basic load point indices and system performance indices. Artificial neural network is recently established as a useful and much promising too, applied to variety of power systems engineering. This paper presents ANN version for evaluating the reliability of distribution power systems (DPSs), in the proposed algorithm, the ANN used to predicted (RPS) using historical data method constructed according to the backpropagation learning rule. At the same time, System indices such as SAIFI and SAIDI of real distribution system are computed and compared with results generated by network method. The result obtained by proposed method gives acceptable reliability indices and can also found that the deviation of computed values by the proposed method is less than 1% and needs running time on ASUN network environment of less than 2 s. The ANN approach demonstrates advantage over the network method.
机译:为了评估配电系统的可靠性,越来越多的研究人员将注意力转向人工智能方法,并且提出了一些可靠性指标,例如基本负荷点指标和系统性能指标。近年来,人工神经网络被确立为一种有用且很有前途的技术,应用于各种电力系统工程。本文提出了一种用于评估配电系统(DPS)可靠性的ANN版本,在所提出的算法中,使用了根据反向传播学习规则构造的历史数据方法来预测(RPS)的ANN。同时,计算实际分配系统的SAIFI和SAIDI等系统指标,并将其与网络方法生成的结果进行比较。所提出的方法得到的结果给出了可接受的可靠性指标,并且还发现所提出的方法的计算值偏差小于1%,并且在ASUN网络环境下的运行时间小于2 s。 ANN方法展示了优于网络方法的优势。

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  • 来源
    《Journal of electrical and computer engineering》 |2012年第1期|p.560541.1-560541.5|共5页
  • 作者单位

    Faculty of Electrical and Electronics Engineering, University of Malaysia, Pahang, Lebuhraya Tun Razak,Kuantan Pahang, 26300 Gambang, Malaysia;

    Faculty of Electrical and Electronics Engineering, University of Malaysia, Pahang, Lebuhraya Tun Razak,Kuantan Pahang, 26300 Gambang, Malaysia;

    National Company, Engineering, Transmission Division TNB, Kuala Lumpur, Malaysia;

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  • 入库时间 2022-08-18 00:50:51

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