A probabilistic power flow calculation method for wind power systems based on the minimum input error criterion

Abstract

The invention discloses a wind-power-system-containing probabilistic load flow calculation method based on a minimum input error criterion. The method includes the following steps that firstly, original data is acquired; secondly, probabilistic modeling is performed on uncertain factors so that a reference distribution function can be acquired; thirdly, based on the minimum input error criterion, the optical calculation order kopt is judged; fourthly, a kopt order half invariant and Gram-Charlier series expansion are adopted for calculating probabilistic load flow. By means of the method, the series and the order number making input errors minimum can be fast determined, influences of the uncertain factors on the fitting effect of the distribution function are comprehensively considered, and therefore on the premise that calculation precision is guaranteed, wind-power-system-containing probabilistic load flow calculation efficiency is improved.

Description

technical field [0001] The invention relates to the field of power system probability power flow, in particular to a method for selecting middle order of semi-invariant and Gram-Charlier series expansion probability power flow calculation. Background technique [0002] Wind power grid connection brings more uncertain factors to the power system. In order to describe the impact of power system uncertain factors on the safe and economic operation of the system, the probability flow method can effectively judge the state variables of the system under the influence of various uncertain factors. It is of great significance to make a backup plan for the scheduler and maintain the normal operation of the system. [0003] Monte Carlo adopts large-scale sampling technology to obtain the random state of the whole system by sampling, and calculates the power flow for each state. The result is accurate but the amount of calculation is too large. The semi-invariant method and the Gram-C...

AI Technical Summary

Problems solved by technology

Considering the diversity and complexity of uncertain factors in the power system, it is difficult to accurately evaluate the impact of different uncertain factors on input errors by using traditional probability flow methods, and there is a lack of unified error indicators to evaluate the Gram-Charlier series relative to the original The fitting effect of random variables, resulting in confusion in order selection and low calculation efficiency

Images