Transformer state anomaly detection method driven by electric energy quality monitoring data
A power quality monitoring, data-driven technology, applied in the direction of instruments, complex mathematical operations, calculations, etc., can solve problems such as difficult state identification algorithm association, inconvenient transformer state detection, etc., to achieve the effect of simplifying complex correlation and realizing detection
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[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will be described in conjunction with the embodiments.
[0032] A power quality monitoring data-driven transformer state abnormality detection method comprises the following steps:
[0033] Step S001: Detect the electrical quantity data of the transformer in the normal state, accumulate the electrical quantity data of the transformer in the normal state to establish a database, standardize the characteristic attributes of the electrical quantity data in the electrical quantity database, and obtain the characteristics of the electrical quantity data attribute value;
[0034] Step S002, using a clustering algorithm to divide the characteristic attribute values of the standardized electrical quantity data into several clusters;
[0035] Step S003, using the median in each cluster as the new cluster center, clustering the characteristic attribute values ...
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