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A new approach to determine subtransient parameters of synchronous machine using wavelet transform and artificial neuron network

机译:一种使用小波变换和人工神经元网络确定同步机次旋转器参数的新方法

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Based on the wavelet transform and artificial neuron network, a new method is presented to determine parameters of synchronous machine from sudden line-to-line short-circuit test. An appropriate wavelet function is chosen to analyze the sudden line-to-line short-circuit transient current and the DC and fundamental component of the transient short-circuit currents are obtained by the wavelet transform. Compared with the traditional methods, the analysis using wavelet transform is more effective. Thus, the precise transient parameters and aperiodic component time constant are estimated by the artificial neuron network. At last, the simulation of sudden line-to-line short-circuit are carried out in a three-phase synchronous machine. The simulation results show that the method developed in this paper is valid.
机译:基于小波变换和人工神经元网络,提出了一种新方法,以确定突然线到线短路测试的同步机的参数。选择适当的小波功能以分析突然的线到线短路瞬态电流,并且通过小波变换获得瞬态短路电流的直流和基本分量。与传统方法相比,使用小波变换的分析更有效。因此,通过人工神经元网络估计精确的瞬态参数和非周期性分量时间常数。最后,在三相同步机中执行突然线到线短路的仿真。仿真结果表明本文开发的方法有效。

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