为了确保电力系统建模的精确性和安全稳定分析的可靠性，进行发电机励磁系统参数辨识测试是一项重要的工作。
To ensure the accuracy of power system modeling and reliability of stabilization analysis, it is essential to test the parameters of generator excitation system.
本文对电力系统中的不良和错误数据的检测辨识与修正进行了深入研究。
In this dissertation, the detection, identification and correction of the bad or wrong data in the power system were investigated thoroughly.
分析了如何利用测量数据构造矩阵束并进行电力系统振荡模态辨识的方法。
Method of power system oscillation mode identification utilizing matrix pencil constructed from sampled data is presented.
电力系统状态估计的内容包括：网络拓扑分析、网络可观测性分析、状态估计、状态估计潮流、不良数据检测和辨识等。
The power system state estimation involves network topology, network observability analysis, state estimation and detection and identification of bad data.
本文将随机系统状态模型辨识技术用于电力系统负荷预报。
Power system load forecasting using stochastic system state model identification technique is proposed.
准确地辨识同步电机参数，是研究分析电力系统运行和控制系统设计的前提。
It is the precondition for investigating power system running and controlling system design to determine the electromagnetic parameters of synchronous electric machine exactly.
本文用最小二乘递推算法辨识自回归模型和自回归动平均模型，编制了电力系统短期在线负荷予报程序。
This paper uses the recursion algorithm of the least-square method to identify an autoregression model and an autoregression moving average model.
该方法对噪声鲁棒性好，能准确辨识各复合振荡模式，有助于电力系统强非线性模式分析，便于在线监测应用。
The proposed method can identify composite modes and strongly time-varying modes accurately with good noise robustness, catering for online analysis.
该方法对噪声鲁棒性好，能准确辨识各复合振荡模式，有助于电力系统强非线性模式分析，便于在线监测应用。
The proposed method can identify composite modes and strongly time-varying modes accurately with good noise robustness, catering for online analysis.
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