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

机译:神经网络观测器在利用运行数据在线估计凸极同步发电机动态参数中的应用

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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.
机译:参数识别对于电力系统中的现代控制策略至关重要,这被认为是动态性能和能效的电力系统。本文介绍了ANN观察者在使用时域,在线干扰测量估算和跟踪凸极同步发电机动态参数的新颖应用。培训ANN观察者的数据通过在一机无限公交车环境中运行的凸极同步发电机的离线模拟来获得。 Levenberg-Marquardt算法已经采用和同化进入训练前馈神经网络的背传播学习算法。 ANNS的输入符合其动态行为中同步发电机动态参数的可观察性分析的结果。开发了一个具有相同输入但不同输出的ANN的集合以确定一组动态参数。 ANNS用于通过分别在每种故障内执行的测量来估计动态参数。培训的ANNS用在线测量测试以识别动态参数。仿真研究表明ANN观察者具有识别凸极同步发电机的动态参数的能力。结果还表明,可以获得给出估计每个动态参数的更好结果的测试。

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