Method for predicting remaining service life of power transformer

A technology for power transformers and life prediction, applied in the field of transformers, can solve problems such as difficulty in obtaining probability density functions, and achieve the effects of accurate prediction results and improved reliable information

Active Publication Date: 2018-04-20
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

Based on the obtained data, the former uses machine learning to fit the evolution law of equipment performance characteristics, and then derives the failure threshold to realize RUL prediction. However, this method is difficult to obtain the probability density function that reflects the random and uncertain characteristics of RUL.

Method used

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  • Method for predicting remaining service life of power transformer
  • Method for predicting remaining service life of power transformer
  • Method for predicting remaining service life of power transformer

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Embodiment Construction

[0033] 1. Multiparameter degradation model of multivariate Weibull distribution

[0034] With the continuous improvement and improvement of design, manufacturing methods, material properties, etc., the failure mechanism of large equipment is becoming more and more complex, often manifested by multiple performance characteristics, and these performance characteristics will produce different degradation trends over time. The degradation processes of various performance characteristics may be independent or have certain dependencies. Therefore, it is of practical significance to study the reliability modeling of equipment with multiple performance characteristics degradation.

[0035] Assuming that the device has K performance characteristics x 1 ,...,x K , and X(t)=[x 1 (t),...,x K (t)] obey the following multivariate Weibull distribution:

[0036]

[0037] In the formula, η(t)=[η 1 (t),...,η K (t)] is the scale parameter; β(t)=[β 1 (t),...,β K (t)] is a shape paramet...

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Abstract

The invention discloses a method for predicting the remaining service life of a power transformer. The method comprises the following steps: a, building a multivariate Weibull distribution model; b, solving the partial derivative of F(.) to obtain a joint probability density function f(.); c, obtaining a log-likelihood function of the f(.); d, obtaining experimental data of degenerative characteristic parameters of an insulating oil paper of the transformer at different times; e, calculating the minimum value of the log-likelihood function to obtain estimated values of the parameters; f, fitting the model parameters to obtain functions of time t, of the parameters; g, obtaining a reliability function expression; and h, predicting the remaining service life of the transformer according to the reliability function. The method disclosed by the invention has the advantages that the remaining service life of the power transformer is predicted according to the degradative characteristic parameters of the insulating material; the probability density function reflecting the random uncertain characteristics of the remaining service life can be conveniently obtained; the prediction results are accurate and reasonable; and reliable information can be provided for the state maintenance or the predictive maintenance of the transformer.

Description

technical field [0001] The invention relates to a method capable of accurately predicting the remaining service life of a power transformer, which belongs to the technical field of transformers. Background technique [0002] Power transformer is one of the safety-critical equipment in the power system. Its traditional maintenance methods mainly include post-failure repair and regular maintenance, but it is easy to cause problems such as "insufficient maintenance" or "overmaintenance". Therefore, in recent years, there has been a tendency to be gradually replaced by condition-based maintenance or predictive maintenance. The remaining useful life (Remaining Useful Life, RUL) prediction is one of the key issues in the realization of power transformer fault prediction and health management (Prognostic and Health Management, PHM). "An important way to change. In addition, the remaining life prediction of major products and major facilities is one of the frontier technologies th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/00G06Q10/04G06Q50/06G06F17/50
CPCG06Q10/04G06Q10/20G06Q50/06G06F2111/08G06F2119/04G06F30/20
Inventor 李刚于长海曹瑞崔敏刘云鹏
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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