Power equipment state monitoring data clustering method and system

Abstract

The invention discloses a power equipment state monitoring data clustering method and system, and the method comprises the steps: obtaining a to-be-clustered data set Xm * n which comprises m samples,enabling each sample to comprise n types of variables, and standardizing the n types of variables of each sample; setting a density threshold parameter k of the DBSCAN model; drawing a k distance mapaccording to the distance between each sample in the standardized Xm * n and other samples; determining a lower limit threshold E0 of the neighborhood radius parameter E according to the category number of the clustering result of a DBSCAN (0, k) model; determining an upper limit threshold Emax of E according to the k distance graph; drawing a'category number-E 'curve according to the E0 and theEmax; and determining the optimal value of E according to the'category number-E 'curve. Optimization of a neighborhood radius parameter E and a density threshold parameter k of a DBSCAN model is achieved by designing and drawing a category number-E curve, and the DBSCAN model is used for clustering power equipment online monitoring data and used for pattern recognition and anomaly detection of online monitoring real-time data and judging normal data categories and anomaly types.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and abnormality detection, and in particular relates to a method and system for clustering data of state monitoring of electric power equipment. Background technique [0002] Power equipment is the most important core part of the smart grid, and its normal operation is the fundamental guarantee of grid security. The power equipment in the smart grid includes: large power transformers: transmission and distribution networks (overhead lines, cable tunnels); relay protection and control equipment, and may also include generators and other equipment. With the rapid development of network technology, sensor technology and computer technology, from the analysis of the latest smart grid research trends, the use of artificial intelligence methods for online monitoring and condition maintenance of power equipment has become a development trend in this field. [0003] The DBSCAN (Density-Based ...

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Problems solved by technology

[0004] In the DBSCAN model, the neighborhood radius parameter E and the density threshold parameter k have a significant impact on the performance of the DBSCAN model, but under the unsupervised condition of lack of data labels, the cross-validation method cannot be used for parameter optimization, and the traditional k-based The optimization model of the distance map will make the parameter optimization results show strong subjectivity and inaccuracy

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