Analysis of dissolved gases in oil is a common condition detection means for power transformer and it has played a significant role in operation and maintenance of power transformer. However

the due to the reliability of online monitoring system of the dissolved gases in the oil as well as site interference

there are plenty of false and wrong data in the monitoring data

which always leads to misjudgment. In response to this problem

the monitoring system often uses a threshold method based on data distribution statistics to determine the authenticity of the data. Since it is difficult to grasp the rules of the data distribution in advance

resulting in the problem of generally low detection rate of abnormal data. In this paper

according to the time series characteristics of on-line monitoring data of dissolved gas in the oil

a kind of an abnormal data detection method based on condensed hierarchical clustering is proposed.First

the sliding time window is used to preprocess a variety of gas monitoring data in the oil to obtain a time series set of monitoring data. Then

such index as average

threshold

standard deviation

wavelet transform periodic components are comprehensively applied to classify them and establish abnormal types of typical time series map. On this basis

the agglomerated hierarchical clustering model is used to perform similarity clustering on the distance between different characteristic data points and the typical abnormal map so to determine the abnormal type of the monitoring data.The verification of the application of actual monitoring data shows that this method can detect abnormal data in the online monitoring data stream and determine its type in real time. It is simple and easy to operate

and has good theoretical and application value.