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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

Pending Publication Date: 2021-01-05
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID NINGXIA ELECTRIC POWER COMPANY +1
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  • Application Information

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Problems solved by technology

However, the existing transformer status anomaly detection algorithm mainly uses non-electrical quantity data such as top oil temperature, load, methane (CH4) volume fraction, ambient temperature, acetylene (C2H2) volume fraction, etc. It is difficult to directly associate the state recognition algorithm with harmonic monitoring, dispatching and other systems for the accumulated massive transformer electrical quantity monitoring data, which brings a lot of inconvenience to the state detection of transformers

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  • Transformer state anomaly detection method driven by electric energy quality monitoring data
  • Transformer state anomaly detection method driven by electric energy quality monitoring data
  • Transformer state anomaly detection method driven by electric energy quality monitoring data

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Embodiment Construction

[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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Abstract

The invention relates to a transformer state anomaly detection method driven by the electric energy quality monitoring data. Transformer electric quantity monitoring data in an existing detection system is utilized, the clustering algorithm is adopted, the complex correlation relationship among the electric quantities is simplified, big data with obvious deviation can be well processed, and detection of transformer state anomaly is effectively achieved.

Description

technical field [0001] The invention relates to the technical field of electric power detection, in particular to a method for detecting abnormal state of a transformer driven by power quality monitoring data. Background technique [0002] As the key hub equipment of the power system, the safe and stable operation of the power transformer is the necessary basis for ensuring the normal supply of high-quality power and the normal operation of social life. The traditional planned maintenance method is time-consuming and laborious, while the condition-based maintenance of power transformers is a new type of maintenance method based on the evaluation of the equipment status and the prediction of the status development trend. This maintenance method can improve the reliability of the equipment while effectively avoiding Insufficient maintenance and excessive maintenance. The key to using the condition-based maintenance method is to realize the state estimation of the power transf...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06F17/18
CPCG06F17/18G06F18/22G06F18/23213
Inventor 罗海荣韩宏伟李刚高博闫振华怡凯黄鸣宇张庆平李学锋马一鸣李永亮徐丽娟
Owner ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID NINGXIA ELECTRIC POWER COMPANY