A Hybrid Strategy-Based Filling Method for Power Missing Data

A technology with missing data and mixed strategies, which is applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as increasing the amount of algorithm calculation, filling accuracy needs to be improved, and modeling without data itself, so as to simplify the difficulty of calculation , Improve accuracy, improve the effect of filling accuracy

Active Publication Date: 2022-04-26
GUANGDONG POWER GRID CO LTD +1
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  • Abstract
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  • Claims
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Problems solved by technology

[0009] Defects of the above-mentioned patent scheme 1: This method designs a solution based on the traditional k-Means algorithm for the problem of missing data filling, which solves the problem to a certain extent, but does not overcome some defects of the k-Means algorithm itself, and uses aggregate There is no way to learn the inherent laws of the data for data filling, and the filling accuracy needs to be improved
[0010] The defect of the above-mentioned patent scheme 2: This method designs an incomplete data filling scheme based on k-Means clustering and deep autoencoder, which solves the problem to a certain extent, but only considers clustering when filling data The resulting full data weighted average does not model the data itself
Moreover, the use of deep autoencoders and backpropagation calculations will increase the computational complexity of the algorithm

Method used

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  • A Hybrid Strategy-Based Filling Method for Power Missing Data
  • A Hybrid Strategy-Based Filling Method for Power Missing Data
  • A Hybrid Strategy-Based Filling Method for Power Missing Data

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Embodiment Construction

[0043] The accompanying drawings are for illustrative purposes only, and should not be construed as limitations on this patent; in order to better illustrate this embodiment, certain components in the accompanying drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art It is understandable that some well-known structures and descriptions thereof may be omitted in the drawings. The positional relationship described in the drawings is for illustrative purposes only, and should not be construed as a limitation on this patent.

[0044] figure 1 It is an overall flow chart of the present invention, comprising the following steps:

[0045] S1. Using the improved k-Means clustering algorithm to cluster the data sets containing missing data;

[0046] S2. Improve and construct the RBF neural network according to the clustering results;

[0047] S3. Train the RBF neural network, and perform a filling test on the...

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Abstract

The present invention relates to the technical field of electric power data cleaning, and more particularly, relates to a method for filling missing electric power data based on a hybrid strategy. Including the following steps: S1, using the improved k-Means clustering algorithm to cluster the data sets containing missing data; S2, improving and constructing the RBF neural network according to the clustering results; S3, training the RBF neural network, and correcting the missing data Do a filling test. This method better solves the clustering problem of data sets with missing attributes, and combines the clustering results to design the RBF neural network to predict and fill the missing values. This method improves the accuracy of missing data filling, is simple to implement, and has an appropriate calculation overhead. It has a high potential for the problem that a large amount of data generated in the process of power system operation and maintenance is missing or damaged due to factors such as physics and software. Practical value.

Description

technical field [0001] The present invention relates to the technical field of electric power data cleaning, and more particularly, relates to a method for filling missing electric power data based on a hybrid strategy. Background technique [0002] With the development of computer science, more and more traditional industries are combined with computer applications. Under the development trend of big data and artificial intelligence, the research on the power industry has produced more new ideas. A large amount of data will be generated in the process of operation and maintenance of the power system, and the problem of data loss will occur due to the influence of physical factors and software factors in the process of data collection, data storage, analysis and classification. Data loss is a complex problem in many research fields. For data mining, the existence of missing values ​​has the following effects: the system loses a lot of useful information; the uncertainty sh...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/08G06F18/23213
Inventor 曾瑛李星南李伟坚林斌刘新展张正峰
Owner GUANGDONG POWER GRID CO LTD
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