Breeze region division-based improved neural network overhead line wind speed prediction method

Abstract

The invention discloses a breeze region division-based improved neural network overhead line wind speed prediction method. The method includes the following steps that: S1, original data are preprocessed; S2, whether a current region is a breeze region is judged through using a mathematical discrimination method, if the current region is a breeze region, the method shifts to S3, if the current region is not a breeze region, the method shifts to S4; S3, wind speed data of the breeze region and corresponding time data are extracted, the extracted data are screened, cleaned and normalized; S4, the data are substituted into an RBF (radial basis function) neural network, so that prediction can be performed; and S5, wind speed prediction data are adopted as the wind speed prediction value of an overhead line. According to the method, the concept of the breeze region is defined at first; since the wind speed fluctuation in the breeze region is small, the neural network can have good fitting performance; and the RBF (radial basis function) neural network method is adopted to perform prediction; and the prediction errors of breeze are reduced under a poor external meteorological condition, and therefore, the accuracy of a prediction result is improved, and the safety of prediction can be improved.

Description

technical field [0001] The invention relates to the technical field of data discrimination and RBF (radial basis function) neural network prediction, in particular to an improved neural network wind speed prediction method for overhead lines based on division of breeze zones. Background technique [0002] Overhead transmission lines are an important part of power grid transmission and distribution. In recent years, under the background of my country's rapid economic development and the improvement of people's quality of life, the society's demand for electricity has increased rapidly, and the requirements for power supply reliability have also become higher. In the early stage of line planning and design, it is formulated according to the needs of users for a certain period of time. Therefore, the planning and development of power grids seriously lag behind the requirements of users for electric energy. In developed areas, power shortage often becomes a bottleneck restricting...

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Problems solved by technology

[0004] Wind speed is one of the most important meteorological factors affecting the current-carrying capacity of overhead transmission lines. In summer with no wind or light wind, on the one hand, the power load will increase greatly, which will greatly increase the current of transmission lines; on the other hand, the weather conditions will be severe. This reduces the current-carrying capacity of overhead transmission lines, which poses a severe challenge to the power transmission and distribution of the grid, so a method that can accurately predict wind speed is needed

However, due to the relatively large fluctuation of wind speed, the general neural network method will have a large error

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