Multi-station fusion system operation situation anomaly monitoring and alarm merging method

Abstract

The invention relates to the field of intelligent power grids, in particular to a multi-station fusion system operation situation anomaly monitoring and alarm merging method, which comprises the stepsof training an anomaly detection model based on historical data of a power multi-station fusion system, and determining an abnormal value threshold; obtaining time sequence data of the operation situation of the power multi-station fusion system in real time, inputting the processed time sequence data into a trained anomaly detection model to obtain an anomaly score of the data, comparing the anomaly score with an anomaly value threshold, and determining anomaly information; based on the historical alarm data information, mining the correlation between the alarm information, and determining an association rule; and determining the category and the emergency degree of the anomaly information, generating corresponding alarm information, and completing combination and output of the alarm information based on the association rule. According to the invention, combination and output of alarm information are realized, and effective detection of anomaly data in time series data in operation and maintenance of the power multi-station fusion system is realized.

Description

technical field [0001] The invention relates to the field of smart grids, in particular to a method for abnormal monitoring and alarm merging of a multi-station fusion system. Background technique [0002] Anomaly detection is about finding data patterns that do not conform to expected behavior, and has a wide range of application scenarios in real life. The safe and stable operation of the power system is very important, so it is necessary to detect abnormalities in the smart grid system in order to deal with potential safety hazards or solve existing problems in a timely manner. At the same time, due to the complex temporal and spatial dimensions of the collected power system operating data and the large amount of calculation, most traditional methods are difficult to capture the complex temporal and spatial diversity of the power system, and it is difficult to apply to large-scale online detection systems to achieve rapid and real-time Power system anomaly detection. ...

AI Technical Summary

Problems solved by technology

If multiple abnormalities are detected in a continuous period of time, each abnormality will send out an alarm message. Massive alarm information is too much work for the staff and it is difficult to check it in time. It is easy to cause delay or omission of important alarm information, causing serious damage. loss

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