Application of neural network observer for on-line estimation of salient-pole synchronous generators' dynamic parameters using the operating data

O. Shariati, A. Zin, M. Aghamohammadi
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引用次数: 12

Abstract

Parameter identification is critical for modern control strategies in electrical power systems which is considered both dynamic performance and energy efficiency. This paper presents a novel application of ANN observers in estimating and tracking Salient-Pole Synchronous Generator Dynamic Parameters using time-domain, on-line disturbance measurements. The data for training ANN Observers are obtained through off-line simulations of a salient-pole synchronous generator operating in a one-machine-infinite-bus environment. The Levenberg-Marquardt algorithm has been adopted and assimilated into the back-propagation learning algorithm for training feed-forward neural networks. The inputs of ANNs are organized in conformity with the results of the observability analysis of synchronous generator dynamic parameters in its dynamic behavior. A collection of ANNs with same inputs but different outputs are developed to determine a set of the dynamic parameters. The ANNs are employed to estimate the dynamic parameters by the measurements which are carried out within each kind of fault separately. The trained ANNs are tested with on-line measurements to identify the dynamic parameters. Simulation studies indicate the ANN observer has a great ability to identify the dynamic parameters of salient-pole synchronous generator. The results also show that the tests which have given better results in estimation of each dynamic parameter can be obtained.
神经网络观测器在显著极同步发电机动态参数在线估计中的应用
电力系统既要考虑动态性能又要考虑能源效率,参数辨识是现代控制策略的关键。本文提出了一种基于时域在线扰动测量的人工神经网络观测器在估计和跟踪显著极同步发电机动态参数方面的新应用。训练人工神经网络观测器的数据是通过在单机无限总线环境下运行的凸极同步发电机的离线模拟获得的。Levenberg-Marquardt算法被引入到反向传播学习算法中,用于训练前馈神经网络。根据同步发电机动态特性参数的可观测性分析结果对人工神经网络的输入进行组织。开发了一组具有相同输入但不同输出的人工神经网络来确定一组动态参数。利用人工神经网络分别对每一类故障进行测量,估计其动态参数。对训练好的人工神经网络进行在线测试,以识别动态参数。仿真研究表明,该观测器对显著极同步发电机的动态参数具有较强的辨识能力。试验结果表明,该方法对各动态参数的估计效果较好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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