Dynamic heat setting value probability distribution predication method of overhead power transmission line based on quantile regression

Abstract

The invention discloses a dynamic heat setting value probability distribution predication method of an overhead power transmission line based on quantile regression. The dynamic heat setting value probability distribution predication method comprises the following steps: taking an uncertain factor of a dynamic heat setting value at the next moment and a historical heat setting value as input variables, and establishing a quantile regression function model; determining a mean value and a median of an input sample, and solving a parameter estimated value of the quantile regression function model; substituting the parameter estimated value into the quantile regression function model, and carrying out quantile regression fitting on sample data by adopting a linear condition quantile to solve different quantiles of the dynamic heat setting values of the power transmission line; and carrying out continuous evaluation on the dynamic heat setting values of the power transmission line in the future moment to obtain complete probability distribution of the dynamic heat setting values of the power transmission line in the future moment. With the adoption of the dynamic heat setting value probability distribution predication method, the dynamic heat setting values in the future moment are predicated and the uncertainty of the dynamic heat setting values is described, so that a good point predication value can be obtained, and a fluctuation interval of the dynamic heat setting value is analyzed; and finally, the whole probability distribution is obtained.

Description

technical field [0001] The invention relates to a quantile regression-based method for predicting the probability distribution of dynamic thermal values ​​of overhead transmission lines. Background technique [0002] With the development of the modern power industry, the access of a large amount of renewable energy power generation and the intelligence of the power grid have become new features of the modern power industry. The transmission capacity of transmission lines is an important bottleneck that restricts the development of power grids, especially the long-distance transmission of new energy power generation. Traditionally, the transmission capacity of transmission lines is defined as the deterministic maximum transmission current of the line, which is a conservative value obtained under the assumption of severe weather conditions to meet the ground safety and operating life of the line. Dynamic Thermal Rating (DTR) technology dynamically improves transmission capaci...

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Problems solved by technology

In the operating environment of most transmission lines, especially the critical lines limited by heat, the wind speed is often lower than 2m / s. For the current wind speed prediction technology, the prediction error of low wind speed is much higher than that of high wind speed. This also becomes a bottleneck for predicting thermal set values ​​through climate parameter models

With the help of time series to directly predict the thermal value, the impact of climate factors on the dynamic thermal value of the transmission line cannot be reflected in the prediction model. Due to the strong fluctuation of the thermal value and the large prediction error, only the thermal Forecast, lack of description of the uncertainty of the thermal value of the future moment, difficult to be transferred to the department to adopt

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