In this article, an online approach for joint parameter estimation of the exciter and Synchronous Generator (SG) is proposed. It only utilizes the Phasor Measurement Unit (PMU) based measurements, which can be obtained at the SG's terminal and does not require field measurements (as in brushless exciters). Compared to existing joint estimation approaches, the proposed work includes multiple aspects to put forward a pragmatic estimation framework. Firstly, the unobservable erroneous initial states are duly considered by utilizing the proposed Augmented Parameter Vector (APV). It is an essential feature in practical scenarios as an accurate representation of a dynamic system entails knowledge of true initial states and parameters. Secondly, suitable models of SG and exciter necessitate the inclusion of saturation characteristics, which is also considered in present work. Further, the estimation procedure is independent of multiple pre-tuned parameters, as such dependence may lead to tedious implementation and inaccurate estimates. Additionally, Wavelet denoising is used to improve the parameter estimation accuracy by pre-processing the noisy measurements prior to estimation. Real-Time Digital Simulator (RTDS) based simulation results, considering noise and measurement rate analogous to commercial synchrophasors, demonstrate the proposed approach's improved accuracy.

中文翻译：

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基于在线测量的同步发电机和励磁机联合参数估计

本文提出了一种用于激励器和同步发电机（SG）联合参数估计的在线方法。它仅利用基于相量测量单元（PMU）的测量，该测量可在SG的终端上获得，并且不需要现场测量（如无刷激励器中的测量）。与现有的联合估计方法相比，本文提出的工作包括多个方面，提出了一个实用的估计框架。首先，通过利用提出的增强参数矢量（APV）适当地考虑了不可观察的错误初始状态。这是实际情况中的基本功能，因为动态系统的准确表示需要了解真实的初始状态和参数。其次，SG和激励器的合适模型必须包含饱和特性，目前的工作中也考虑了这一点。此外，估计过程独立于多个预先调谐的参数，因为这种依赖性可能导致繁琐的实现和不准确的估计。另外，小波去噪用于通过在估计之前对噪声测量值进行预处理来提高参数估计的准确性。基于实时数字仿真器（RTDS）的仿真结果，考虑到与商用同步相量类似的噪声和测量速率，证明了该方法的改进精度。小波去噪用于通过在估计之前对噪声测量进行预处理来提高参数估计的准确性。基于实时数字仿真器（RTDS）的仿真结果，考虑到与商用同步相量类似的噪声和测量速率，证明了该方法的改进精度。小波去噪用于通过在估计之前对噪声测量进行预处理来提高参数估计的准确性。基于实时数字仿真器（RTDS）的仿真结果，考虑到与商用同步相量类似的噪声和测量速率，证明了该方法的改进精度。