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A new artificial neural network based method for islanding detection of distributed generators

机译:一种新的基于人工神经网络的分布式发电机孤岛检测方法

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This paper presents an artificial neural network (ANN) based method for islanding detection of distributed synchronous generators. The proposed method takes advantage of ANN as pattern classifiers. It is capable of identifying the islanding condition based on samples of the voltage waveform measured at the distributed generator terminals only, which is an important advantage over other ANN-based anti-islanding methods. Moreover, the proposed method is robust against false operation. In order to create a training data set for the ANN, a data selection procedure has been proposed, so that the ANN could be trained more effectively, which has contributed positively to the good performance of the method. The concept of the time-performance region has been introduced to assess the method performance, as well as the non-detection zones. A detailed discussion about the data sampling rate to feed the proposed method has also been conducted, so that the computational burden can be faced as an important factor to assess its performance. (C) 2015 Elsevier Ltd. All rights reserved.
机译:本文提出了一种基于人工神经网络的分布式同步发电机孤岛检测方法。所提出的方法利用ANN作为模式分类器。它能够仅基于分布式发电机终端处测得的电压波形样本来识别孤岛状态,这是优于其他基于ANN的反孤岛方法的重要优势。此外,所提出的方法对于错误操作是鲁棒的。为了创建用于神经网络的训练数据集,提出了一种数据选择程序,以便可以更有效地对神经网络进行训练,这为该方法的良好性能做出了积极贡献。引入了时间性能区域的概念来评估方法性能以及非检测区域。还对提供该方法的数据采样率进行了详细讨论,因此计算负担可以作为评估其性能的重要因素。 (C)2015 Elsevier Ltd.保留所有权利。

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