Novel approaches for online modal estimation of power systems using PMUs data contaminated with outliers
Novel approaches for online modal estimation of power systems using PMUs data contaminated with outliers
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DOI：
10.1016/j.epsr.2015.03.004
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发表时间：
2015-07
影响因子：
3.9
通讯作者：
M. Farrokhifard;M. Hatami;M. Parniani
中科院分区：
文献类型：
--
作者：
M. Farrokhifard;M. Hatami;M. Parniani
在利用PMU数据进行电力系统模态估计时，最重要的问题之一是异常值的负面影响。因此，除了分析PMU数据的技术之外，实施某种方法来克服这些离群值的必要性是切实可行的。本文的目的是提出不同的方法来克服离群值，并准确地估计系统的机电模式时，有怀疑，PMU数据可能被污染的不一致的测量。所提出的方法通常分为两大类：第一类在预处理阶段自适应地检测和修改离群值，然后准备修改后的数据进行处理。第二类包括通过处理数据和解决问题来检测离群值的鲁棒方法。基于自适应数据预处理与不同的数据修改方法，另一种方法，在本研究中提出的情况下，参数技术用于模态估计。在十六机五区域测试系统上研究了所提出方法的效率。假设所有的模拟数据集都被少数样本中的离群值所扭曲。最后，对各种方法的计算结果进行了比较，并讨论了各种方法的应用。
One of the most important issues in modal estimation of power systems using PMUs data is the negative effect of outliers. Hence, in addition to the techniques of analyzing PMUs data, the necessity of implementing some kinds of approach to overcome these outliers is tangible. This paper aims to present different approaches to overcome outliers and also estimate the electromechanical modes of the system accurately when there is suspicion that the PMUs data may be contaminated by discordant measurements. Proposed approaches are generally categorized into two main classifications: the first category detects and modifies outliers in the pre-processing stage adaptively and then prepares the modified data for processing. The second category consists of robust approaches that detect the outliers through processing data and solving the problem. Based on adaptive data pre-processing with a different data modification method, another approach is proposed in this research for cases where parametric techniques are employed for modal estimation. The efficiency of the proposed methods is investigated on the sixteen-machine five-area test system. It is assumed that all sets of simulated data are distorted by outliers in a few samples. Finally, the results are compared and the application of each method is discussed.