PMU data classification method based on random matrix theory and fuzzy C-means clustering algorithm

Abstract

The invention discloses a PMU data classification method based on a random matrix theory and a fuzzy C-means clustering algorithm. The method comprises the steps of 1) establishing a historical PMU data random matrix model, and performing feature extraction by establishing a linear feature value statistic to obtain a feature data set; 2) clustering the feature data set by using a fuzzy C-means clustering algorithm to obtain various types of clustering centers and membership matrixes; 3) establishing a random matrix model by combining the real-time power grid operation data with historical data, and performing feature extraction by establishing a linear feature value statistic to generate feature data; and 4) initializing by using the result of the step 2), carrying out fuzzy C-means clustering on the feature data generated in the step 3), and judging the category of the real-time data, thereby realizing PMU data real-time classification under data driving.

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 ...

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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

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