High-proportion photovoltaic power distribution network voltage prediction method based on time convolution neural network

Abstract

The invention discloses a high-proportion photovoltaic power distribution network voltage prediction method based on a time convolution neural network. The high-proportion photovoltaic power distribution network voltage prediction method comprises the step of: 1, carrying out data preprocessing on original load data, namely carrying out normalization on voltage time sequence data through employinga maximum and minimum interval scaling method based on multiple time scales to obtain a complete voltage sequence; 2, constructing an input feature vector set, namely performing feature screening based on an extreme gradient boosting tree algorithm of a decision tree, constructing a training sample set, outputting each feature weight, and screening out different feature subsets in combination with the weights and the voltage prediction model condition; and 3, establishing a voltage prediction framework based on the high-proportion photovoltaic power distribution network, and training a time convolution network prediction model to obtain a voltage prediction result. According to the high-proportion photovoltaic power distribution network voltage prediction method, the extracted features are combined with the time and input into different channels of the time convolutional neural network model to obtain the prediction result, so that the purpose of remarkably improving the voltage prediction precision of the power distribution network is achieved.

Description

technical field [0001] The invention relates to the field of voltage prediction of distribution network with high proportion of photovoltaics, in particular to a voltage prediction method of high proportion of photovoltaic distribution network based on time convolutional neural network. Background technique [0002] Under the dual pressure of deteriorating environmental problems and shortage of traditional energy sources, the grid-connected capacity of photovoltaic new energy is increasing rapidly. Problems such as the limitation and other problems are becoming more and more serious. This volatility and intermittent changes are more obvious due to environmental factors such as solar radiation, weather, and temperature. The risk of grid voltage overruns and fluctuations is intensified, which is difficult to solve only by traditional voltage regulation methods. In addition, a high proportion of distributed photovoltaic output mutations will lead to more obvious node voltage f...

AI Technical Summary

Problems solved by technology

Considering that there is a certain regularity in photovoltaic power generation, some potentially valuable information is contained in such a huge amount of data, but it has not been effectively used at present, and it is necessary to rely on artificial intelligence technology to discover the value of data

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