State anomaly detection method of power transmission and transformation equipment based on spatio-temporal joint data clustering analysis
A technology of power transmission and transformation equipment and joint data, applied in text database clustering/classification, structured data retrieval, unstructured text data retrieval, etc., can solve problems such as large volume and difficult equipment status data analysis
Active Publication Date: 2020-02-14
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +1
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The status data of equipment (including online monitoring, electrification detection, preventive test data, etc.) has the characteristics of large volume and variety, and it is difficult for conventional data processing technology to conduct comprehensive and in-depth analysis of equipment status data
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[0087] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.
[0088] Such as Figure 4 As shown, the abnormal detection method of power transmission and transformation equipment status based on spatio-temporal joint data cluster analysis includes the following steps:
[0089] Step S1: Add a time window to the time component of the state data of power transmission and transformation equipment to obtain a time subsequence, and obtain a time-space subsequence by combining the space component and the generated time subsequence;
[0090] Step S2: Use c-means fuzzy clustering FCM to cluster the space-time subsequences according to different time windows to obtain a block matrix; each block matrix describes the classes that exist in the time window corresponding to the block matrix; respectively enter step S3 and step S4;
[0091] Step S3: Calculate the abnormality value of a single spatio-temporal subsequence accordin...
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The invention discloses a space-time union data clustering analysis based abnormal status detection method for a power transmission and transformation device. The method comprises the following steps of adding time windows into time components of status data of the power transmission and transformation device to acquire time subsequences; obtaining space-time subsequences by combining a space component and the generated time subsequences; clustering the space-time subsequences by c mean value fuzzy clustering FCM according to different time windows to obtain block matrixes; describing a category existing in the time window corresponding to the block matrix by each block matrix; calculating an abnormal condition of a single space-time subsequence according to historical normal data of the status of the power transmission and transformation device, giving an abnormality value to the category of each time window by using the block matrix, and judging the abnormal condition of the device status according to the size of the abnormality value; and declaring that the abnormal condition exists in the device status if the abnormality value is high. The relation between categories of different time windows is built by using the block matrixes, and thus, the development condition of an abnormal type is visually observed.
Description
technical field [0001] The invention relates to the technical field of power transmission and transformation equipment detection, in particular to a method for detecting abnormal state of power transmission and transformation equipment through time-space joint data clustering analysis. Background technique [0002] Power transmission and transformation equipment will be affected by abnormal events such as overload, overvoltage, internal insulation aging, and natural environment during actual operation. There is a strong need for detection. [0003] Domestic and foreign literatures have proposed many anomaly detection and status assessment technologies for power transmission and transformation equipment. "Outlier Detection and Distribution Characteristics of Transformer Oil Chromatography Based on MCD Robust Statistical Analysis" adopts MCD robust statistical method to detect outliers, and analyzes the abnormal values and normal data separately, and gives the distribution ...
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IPC IPC(8): G06F16/28
CPCG06F16/285G06F16/35G06F16/355G06Q10/063G06Q10/06313G06Q50/06
Inventor 王辉杜修明杨祎李秀卫朱文兵郑建袁海燕陈玉峰郭志红王进刘兴华
Owner ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY