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Voltage stability monitoring of power systems using reduced network and artificial neural network

机译:使用简化网络和人工神经网络的电力系统电压稳定性监控

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This paper presents network reduction based methodologies to monitor voltage stability of power systems using limited number of measurements. In a multi-area power system, artificial neural networks (ANNs) are used to estimate the loading margin of the overall system, based on measurements from the internal area only. Information regarding the important measurements from the external areas is considered in measurement transformation through the network reduction process, to enhance the estimation accuracy of the ANNs. A Z-score based bad or missing data processing algorithm is implemented to make the methodologies robust. To account for changing operating conditions, adaptive training of the ANNs is also suggested. The proposed methods are successfully implemented on IEEE 14-bus and 118- bus test systems. (C) 2016 Elsevier Ltd. All rights reserved.
机译:本文提出了基于网络缩减的方法,以使用有限的测量次数来监视电力系统的电压稳定性。在多区域电力系统中,仅基于内部区域的测量结果,使用人工神经网络(ANN)估计整个系统的负载裕度。在通过网络缩减过程进行的测量转换中,会考虑与来自外部区域的重要测量有关的信息,以提高ANN的估算精度。实现了基于Z评分的不良或缺失数据处理算法,以使该方法具有鲁棒性。为了说明不断变化的工作条件,还建议对ANN进行自适应训练。所提出的方法已在IEEE 14总线和118总线测试系统上成功实现。 (C)2016 Elsevier Ltd.保留所有权利。

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