A novel technique for identification and condition monitoring of nonlinear loads in power systems

被引:0
|
作者
Gilreath, Phil [1 ]
Peterson, Maryclaire [1 ]
Singh, Brij N. [1 ]
机构
[1] Tulane Univ, Dept Elect Engn & Comp Sci, New Orleans, LA 70118 USA
关键词
real-time condition monitoring; nonlinear loads; unbalanced power systems; centroid; Concordia pattern;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper deals with Centroid-Concordia patterns for characterization of harmonic producing three-phase loads in a power system distribution network The three-phase currents (i(a), i(b), and i(c)) at the Point of Common Coupling (PCC) are sensed and processed through the Concordia mathematical formulation resulting in two-phase orthogonal currents (i(alpha)-i(beta)). The currents (i(alpha)-i(beta)) are used to obtain the Concordia patterns needed for centroid calculation. The centroid of the Concordia pattern reveals characteristics of the load connected at the PCC. In the majority of cases, the developed Concordia patterns do not differ much from each other as the centroid remains at the origin; this leads to a failure if conventional Concordia patterns are used to discern faults and condition monitoring of loads at power system distribution. Therefore, we proposed modified Concordia pattern methods. In the modified Concordia pattern, we exploit pattern symmetry around the alpha-beta axes of the transform currents (i(alpha)-i(beta)). The computed value of the centroid of the modified Concordia pattern can be used to develop a drift pattern of the centroid location. Using the drift pattern over time, deterioration of system condition can be monitored and discerned. The drift pattern will allow us to develop the mathematical formulation for load modeling. This is an important aspect of the proposed investigation due to an increasing proliferation of nonlinear loads on aging power system network The proposed method will be simple, non-invasive, and require reduced data sets and memory; therefore, it may prove to be easier for real-time load modeling, condition monitoring, and condition prediction for an ever changing nature of loads on power system networks.
引用
收藏
页码:1024 / +
页数:2
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