A transformer state evaluation clustering analysis method based on data imbalance measurement

Abstract

The invention discloses a transformer state evaluation clustering analysis method based on data imbalance measurement. The method comprises the following steps of screening out index parameters corresponding to different types of fault analysis of the power transformer from unbalanced monitoring data according to a common fault index system of the power transformer, and processing the index parameters by using a proportional normalization method; randomly selecting two groups of data in the index parameters as an initial clustering center, and setting clustering analysis parameters; calculating the Euclidean distance between each type of fault index parameters and the initial clustering center, dividing data in the unbalanced monitoring data into a lower approximate set or a boundary areaof a class cluster according to the Euclidean distance, and calculating the degree of unbalance between the class clusters; measuring the membership degree of the monitoring data by fusing the class cluster imbalance degree; carrying out iterative computation on the class cluster center according to the class cluster data distribution condition; and finally, carrying out state evaluation on the power transformer according to a clustering result. According to the present invention, the state evaluation precision of the power transformer is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of power transformer state evaluation, and in particular relates to a transformer state evaluation cluster analysis method based on data unbalanced measurement. Background technique [0002] As an important power transmission and transformation equipment, power transformers are distributed throughout the key nodes of the entire power transmission and transformation network. Its safe and stable operation is crucial to ensuring power supply. Once a failure occurs, it will not only cause major economic losses, but also affect social stability. Therefore, by analyzing the monitoring data of power transformers, it is of great significance to evaluate the operation status of power transformers to maintain the safe and stable operation of the entire power system. The traditional state assessment method can better diagnose the fault type of the transformer by analyzing the fault data recorded by the fault recorder....

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem that the existing clustering algorithm has poor effect on processing the power transformer unbalanced monitoring data set, the present invention provides a transformer state evaluation cluster analysis method based on data unbalanced measurement, which is based on common fault indicators of power transformers system, from the unbalanced monitoring data, select the index parameters corresponding to different types of fault analysis of power transformers, and use the method of proportional normalization to process the index parameters; finally, use the rough fuzzy k-means clustering method of unbalanced measurement The power transformer state assessment method performs cluster analysis on unbalanced monitoring data, and obtains the corresponding power transformer state assessment results; the specific technical implementation plan is as follows:

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