Abstract
With the increasing rate of wind power installed capacity, voltage state assessment with large-scale wind power integration is of great significance. In this paper, a vine-copula based voltage state assessment method with large-scale wind power integration is proposed. Firstly, the nonparametric kernel density estimation is used to fit the wind speed distribution, and vine-copula is used to construct the wind speed joint distribution model of multiple regions. In order to obtain voltage distribution characteristics, probabilistic load flow based on the semi-invariant method and wind speed independent transformation based on the Rosenblatt transformation are described. On this basis, a voltage state assessment index is established for the more comprehensive evaluation of voltage characteristics, and a voltage state assessment procedure is proposed. Taking actual wind speed as an example, the case study of the IEEE 24-node power system and the east Inner Mongolia power system for voltage state assessment with large-scale wind power integration are studied. The simulation results verify the effectiveness of the proposed voltage state assessment method.
Highlights
In recent years, the growth rate of wind power installed capacity has been rapid [1]
Probabilistic load flow based on the semi-invariant method and wind speed independent transformation based on the Rosenblatt transformation are described
This paper presents a vine-copula based voltage state assessment method with large-scale wind power integration
Summary
The growth rate of wind power installed capacity has been rapid [1]. We firstly apply the vine-copula function to the voltage state assessment method with large-scale wind power integration. The traditional copula model could only describe the nonlinearity, asymmetry and tail correlation between two random variables, and building higher-dimensional copula is generally recognised as a difficult problem Limited by this reason, vine-copula was proposed by Kjersti Aas [11]. Multivariate data exhibiting complex patterns of dependence in the tails could be modelled using the vine-copula function This function allows inference on the parameters of the pair-copulae on various levels of the construction.
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