Dynamic capacity prediction method for overhead transmission line

Abstract

The invention discloses a dynamic capacity prediction method for an overhead transmission line. The dynamic capacity prediction method comprises the following steps of: measuring a real-time wind speed near a conducting wire through a wind speed sensor mounted on an iron tower, measuring a real-time solar radiation temperature and a real-time ambient temperature through a temperature sensor, predicating the wind speeds and solar radiation temperatures within 1 hour, 2 hours and 4 hours in the future through a RBM depth learning mechanism, obtaining a real-time conducting wire temperature through a tensiometer and a tension-temperature fitting curve, substituting the wind speed, a predicted value of solar radiation temperature and the real-time conducting wire temperature into a conducting line capacity calculation model to obtain predicted capacity values of the conducting wire within 1 hour, 2 hours and 4 hours in the future. Compared with the existing neural network and other prediction methods, the prediction accuracy is effectively improved. The problem that the input of the existing neural network prediction method is extremely limited is solved, and the dimension of the input is greatly improved, and a theoretical basis is provided for the analysis of large grid data in the future.

Description

technical field [0001] The invention relates to the field of dynamic capacity prediction of transmission lines, in particular to a dynamic capacity prediction method for overhead transmission lines. Background technique [0002] The dynamic capacity increase of transmission lines is one of the key technologies to realize the core value and goal of smart grid transmission intelligence. Predicting the dynamic heat capacity of transmission lines online, reasonably arranging the operation mode and scheduling management during peak load periods are of great significance to the safe and economical operation of transmission lines, and are also very good for increasing the access capacity of intermittent renewable energy such as wind power. effect. [0003] Existing forecasting methods such as neural network methods have different forecasting effects, it is difficult to guarantee accuracy, and the input data scale is very limited, so it is difficult to adapt to the massive data ana...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the above problems, provide a dynamic capacity prediction method for overhead transmission lines, use the restricted Boltzmann machine to realize the prediction of wind speed and sunshine radiation temperature, improve the accuracy of prediction, and solve the problem of existing neural network The input volume of the prediction method is extremely limited, which greatly improves the dimension of the input volume

Images