Method for processing transformer DGA online monitoring data

Abstract

The invention provides a transformer DGA online monitoring data processing method, which comprises the following steps: according to the characteristics of returned data, enabling online data to be equivalent to a time sequence; in the first stage, the thought of a sliding window algorithm is introduced, an improved sequence piecewise linearization algorithm is provided, sequence data is divided into a plurality of line segments represented by slopes and spans, then online monitoring data is symbolized by using improved K-means clustering, and finally the relevance among different indexes in DGA is mined by using an APRIORI algorithm, so that the DGA is subjected to online monitoring. An abnormal value existing in the data is explored; and in the second stage, according to the screened abnormal numerical sampling points, an improved particle swarm optimization support vector regression algorithm is used, the solving speed and solving diversity of the algorithm are guaranteed, key parameters in the support vector regression algorithm are optimized, and the sampling points are repaired, so that the processing of the online DGA monitoring data of the transformer is completed.

Description

technical field [0001] The invention relates to a processing method for transformer DGA online monitoring data, which belongs to the field of power equipment data cleaning. Background technique [0002] The power transformer is the key equipment for power conversion and transmission, and its safe and stable operation is an important guarantee for the quality of power supply for users. The online data of the DGA index of the transformer is a real-time monitoring of the insulation performance of the equipment. Based on the analysis of the oil chromatographic data, the real-time state of the transformer can be quickly obtained; at the same time, there are many index dimensions in the DGA data. , helps to identify data with different abnormal patterns in online data, and can enhance the credibility of the comprehensive status evaluation results of equipment. [0003] Due to the operating environment of the equipment and the electromagnetic interference of the transformer itself...

AI Technical Summary

Problems solved by technology

[0003] Due to the operating environment of the equipment and the electromagnetic interference of the transformer itself, the online monitoring device is prone to randomly distributed abnormal value points during the data collection and transmission process, and even data drift and transmission interruption in severe cases.

For obvious data abnormalities such as data drift and data interruption, the background system can quickly identify and issue alarms for the problem; but for those abnormal value points randomly distributed in normal online data, it is very important for the real-time representation of equipment status indicators. Serious interference will also affect the status evaluation work based on indicators, which will easily cause false alarms and false alarms of equipment abnormal status, resulting in waste of equipment operation and maintenance resources

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