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A classification method for pmu data based on random matrix theory and fuzzy c-means clustering algorithm

A random matrix theory and mean value clustering technology, applied in character and pattern recognition, computing, computer components, etc., can solve the problem of large influence, uncertainty of new energy power supply output, difficulty in adapting to complex and changeable online operation mode of power grid to achieve real-time classification and improve classification accuracy and reliability

Active Publication Date: 2022-04-05
WUHAN UNIV +2
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AI Technical Summary

Problems solved by technology

[0003] The output of new energy power is uncertain, and the model-driven PMU data classification method is greatly affected by expert experience and typical operation modes, and it is difficult to adapt to the complex and changeable online operation mode of the power grid

Method used

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  • A classification method for pmu data based on random matrix theory and fuzzy c-means clustering algorithm
  • A classification method for pmu data based on random matrix theory and fuzzy c-means clustering algorithm
  • A classification method for pmu data based on random matrix theory and fuzzy c-means clustering algorithm

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Embodiment Construction

[0049] In the following, the technical solutions of the present invention will be further specifically described through embodiments and in conjunction with the accompanying drawings.

[0050] The invention is a PMU data classification method based on random matrix theory and fuzzy C-means clustering algorithm. Such as figure 1 Shown is the flow chart of the PMU data classification method of the present invention. Specifically, the specific calculation process of a PMU data classification method based on random matrix theory and fuzzy C-means clustering algorithm of the present invention includes the following steps:

[0051] (1) Obtain the historical PMU data of each node in the power system, obtain the voltage phasor information from the PMU data, obtain the original data matrix S, and determine the length and width of the sliding time window at the same time, extract each sliding time window from the original data S Matrix S t , and standardize it to get the standard non...

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Abstract

The invention discloses a PMU data classification method based on random matrix theory and fuzzy C-means clustering algorithm. Including: step 1) establishing a random matrix model of historical PMU data, and extracting features by establishing linear eigenvalue statistics to obtain feature data sets; step 2) clustering feature data sets with fuzzy C-means clustering algorithm to obtain various cluster center and membership matrix; step 3) combine the real-time power grid operation data with historical data to establish a random matrix model, and perform feature extraction by establishing linear eigenvalue statistics to generate feature data; step 4) use the results of step 2 to initialize , perform fuzzy C-means clustering on the feature data generated in step 3, and judge the category of real-time data. The invention can realize real-time classification of PMU data driven by data.

Description

technical field [0001] The invention belongs to the field of power systems, and more specifically relates to a PMU data classification method based on random matrix theory and fuzzy C-means clustering algorithm. Background technique [0002] With the deepening of power grid intelligence, massive PMU data will be continuously transmitted to the monitoring center in the form of data stream, and the monitoring center needs to quickly identify and process the information carried by the PMU data stream. Using data-driven methods to classify PMU data in real time and identify different operating states of the power grid based on the classification results is a new method to effectively utilize PMU data. [0003] The output of new energy power is uncertain, and the model-driven PMU data classification method is greatly affected by expert experience and typical operation modes, and it is difficult to adapt to the complex and changeable online operation mode of the power grid. With ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/2321G06F18/24
Inventor 刘晓莉张帅东王学斌曾祥晖姚磊邓长虹龙志君丁玉杰邹佳芯
Owner WUHAN UNIV
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