【摘要】 高速采样和高精度数字化自动测试仪器应用于大型同步发电机测试,需要计算机辅助分析程序自动分析数据和辨识电机的参数。为了提高参数辨识精度,本文提出了一种全新的动态参数辨识方法,即根据短路电流中衰减分量的性质,推导出优化组合模型分时段采用非线性最小二乘回归方法直接辨识发电机参数的原理。另外,还根据IEEEStd-115和GB/T-1029[1,3]有关发电机突然三相短路电流波形处理方法实现对短路电流上下包络线的拟合以计算大型同步发电机的动态参数。并开发了利用突然三相短路试验数据辨识大型同步发电机动态参数的计算机辅助分析软件。通过实际电机的测试数据处理,验证了软件及算法的实用性。更多还原

【Abstract】 The application of high-speed acquisition and high-precision digital instruments to the test of large synchronous generators needs programs to analyze the data automatically and to identify the parameters of a generator. In order to obtain high precision of parameters estimation, this paper employs a novel method, which is deriving optimal combination models from the characteristics of the decaying-current components of one phase short current, to directly identify the transient parameters in different regions employing nonlinear least squares regression theory respectively, and bases on the IEEE Std-115 and the GB/T 1029 graphical procedure to approximate the parameters by fitting the upper and lower envelopes of the short currents. The software has been developed to automate the analysis of digitized sudden-short-circuit oscillograms and estimate the dynamic parameters. Using the test data of a real generator validate the software and the arithmetic.更多还原