.png){kind=icon}
.png){kind=icon}
Return
Statistical testing for load models using measured data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.epsr.2018.05.026.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper applies the statistical testing theory to examine the validity of different load parametric models. Traditionally measurement-based static load modeling has been performed based on a single parametric model. Commonly utilized models include: ZIP (constant-impedance constant-current constant-power) model, exponential model, and frequency component adjusted ZIP/exponential models. It has been conjectured that the models making use of the frequency feature should be better compared to the ones purely based on the voltage component. However, there has not been any theoretical-based justification to confirm this claim. It is a goal of this paper to provide a formal method for verifying this claim by employing the theory of statistical testing for correct parametric model specification. In particular, a class of F-tests for checking the correctness of the specific load model is employed. Our methodology is verified on the real phasor measurement unit (PMU) data describing a radial load in the Manitoba Hydro power system. The obtained results confirm the usefulness of the frequency component based models.
Keywords:
Load modeling
Statistical testing
F-test
Regression nested-models
Parametric system identification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.2
Papers:
1.1W
Citations:
2.2W