Method for evaluating state of transformer

Abstract

The application discloses a method for evaluating the state of a transformer. The method includes the following steps that: continuous numerical type five-dimensional feature attributes are combined with one-dimensional type class labels to establish a historical knowledge base, wherein the five-dimensional feature attributes include a load rate, three-phase unbalance degree data, a winding temperature rise, an insulation oil temperature rise, and vibration data and the one-dimensional type class labels include a transformer state; a split point is set and the continuous numerical type five-dimensional feature attributes are discretized into Boolean attributes to complete data preprocessing; a decision-making classification model based on Gini index attribute selection measurement is established; on the basis of a cross training method and a self-help training method, the model is evaluated and optimized to obtain a new decision-making classification model; and the state of a transformer is evaluated based on the new decision-making classification model. Comprehensive evaluation is carried out by using the five-dimensional attributes of the electrical quantities including the loadrate and the three-phase unbalance and the non-electrical quantities including the winding temperature rise, insulating oil temperature rise and vibration frequency; and the evaluation parameters become comprehensive based on the decision-making classification method of the Gini index attribute selection measurement, so that the evaluation accuracy is improved effectively.

Description

technical field [0001] The present application relates to the technical field of state evaluation of electric equipment, and in particular to a method for evaluating a state of a transformer. Background technique [0002] As power grid companies continue to promote the application of metering automation systems, and transformer manufacturers promote transformer windings, insulating oil temperature rise, and vibration monitoring, the feasibility of transformer state assessment based on data mining is gradually increasing. At present, the distribution network of Yunnan power grid has achieved full coverage of the metering automation system, and the amount of monitoring information data is huge, but the data integrity is insufficient, the data utilization rate is insufficient, and they are independent and non-interactive. [0003] At present, in terms of transformer evaluation based on the decision tree classification algorithm, the methods used as split attribute metrics mainl...

AI Technical Summary

Problems solved by technology

[0004] This application provides an evaluation method of transformer status to solve the technical problem of low accuracy of existing evaluation methods

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