High-proportion photovoltaic power distribution network voltage prediction method based on time convolution neural network
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A convolutional neural network and voltage prediction technology, applied in neural learning methods, AC network voltage adjustment, biological neural network models, etc., can solve problems such as effective utilization, achieve small memory usage, improve safety and stability, and improve operating economy Effects on Sex and Reliability
Active Publication Date: 2021-03-26
ELECTRIC POWER RES INST OF STATE GRID ZHEJIANG ELECTRIC POWER COMAPNY +2
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Embodiment 1
[0052] Such as figure 1 As shown, a high-proportion photovoltaic distribution network reactive power and voltage prediction method based on time convolutional neural network described in the present invention, the process is as follows figure 1 As shown, it specifically includes the following steps:
[0053] Step 1: Raw load data for data preprocessing
[0054] Said step 1 is as follows:
[0055] The historical operation data of the key nodes of the distribution network in the past year is selected, and the time scale of the predicted voltage is 1h, that is, s=1. The rolling prediction method is used to construct the training feature set. In order to facilitate the time convolutional neural network prediction model training and feature extraction, the voltage time series data is subjected to maximum and minimum normalization processing, so that the original data is located in the [0,1] interval, and the normalization processing formula is as follows:
[0056] (1)
[0...
Embodiment 2
[0088] 1) Establish a network model with a high proportion of photovoltaic distribution network
[0089] Attached picture figure 2 It is an IEEE33 node power distribution system. The system contains 3 photovoltaic power sources, and its nodes 5, 14, and 28 are installed with photovoltaic power sources. The capacity is shown in Table 1.
[0090] Table 1. Node photovoltaic power supply parameters
[0091] installation location 5 14 28 Active power / kW 25 16 45 Reactive power / kvar 8 5 5
[0092] 2) Analysis of historical voltage data of distribution network
[0093] Assume that bus 1 of the system is a balanced node, and the node voltage fluctuates roughly in the range of 220-240V. According to formula (1), the preprocessed voltage sequence of node 16 in about 10 days is obtained, and then the preprocessed data is characterized by XGBoost algorithm Analysis, output the proportion of times each feature is used to split the decision tree, that is...
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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...
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