Fast anomaly detection method for large-scale power transmission and transformation equipment monitoring data streams

A technology for power transmission and transformation equipment and anomaly detection, which is applied to electrical components, transmission systems, character and pattern recognition, etc., can solve problems such as difficult online data flow rapid analysis, and achieve the effect of rapid anomaly detection and rapid analysis

Inactive Publication Date: 2018-03-27
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

However, the traditional offline data analysis method based on batch processing is difficult to c

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  • Fast anomaly detection method for large-scale power transmission and transformation equipment monitoring data streams
  • Fast anomaly detection method for large-scale power transmission and transformation equipment monitoring data streams
  • Fast anomaly detection method for large-scale power transmission and transformation equipment monitoring data streams

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Embodiment Construction

[0033] The technical solutions of the present invention will be further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0034] The invention is an abnormality detection method for large-scale power transmission and transformation equipment monitoring data flow based on the Spark Steaming platform. The method is designed to realize the incremental DBScan abnormality monitoring of large-scale power transmission and transformation equipment monitoring data flow. Such as figure 1 The anomaly detection platform architecture for monitoring data stream shown is the platform architecture used for the anomaly detection method for monitoring data stream in the present invention. The platform architecture includes a front-end processor for obtaining monitoring data streams, a SparkStreaming cluster, a historical data storage unit and an abnormal data alarm module. The present invention clusters and classifies historical data by using...

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Abstract

The invention discloses a fast anomaly detection method for large-scale power transmission and transformation equipment monitoring data streams. The method includes the following steps: performing clustering and category marking on historical data by using a DBScan algorithm, and sending a marked sample to a SparkStreaming cluster to perform real-time incremental clustering; enabling a front-end processor to receive data streams sent by various terminals, pushing the data streams to a Spark Steaming (as shown in the original document) cluster for processing, and implementing real-time featureextraction and normalization processing on the Spark Steaming cluster; and then performing real-time clustering to complete the judgment of a new sample category. By adopting the method, the fast analysis of large-scale data streams of smart power grids can be realized, and the fast anomaly detection of the large-scale power transmission and transformation equipment monitoring data streams can beimplemented.

Description

technical field [0001] The invention relates to the field of large-scale power transmission and transformation equipment monitoring, in particular to a fast anomaly detection method for monitoring data streams of large-scale power transmission and transformation equipment. Background technique [0002] With the advancement of smart grid construction, multiple links in the power system are facing the challenge of rapid analysis of large-scale data flows. Especially in the online monitoring system of power transmission and transformation equipment, as the monitoring scope expands and the depth continues to strengthen, a large number of multi-source heterogeneous monitoring data collected by many measurement and sensing devices are continuously sent to the monitoring center in the form of data streams, forming Large-scale monitoring data flow, and the monitoring center needs to process these streaming data in real time and quickly. [0003] The current mode of testing data ana...

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Application Information

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IPC IPC(8): H04L29/08G06K9/62
CPCH04L67/10H04L67/12H04L67/565H04L67/55G06F18/2321
Inventor 宋亚奇李莉
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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