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Power equipment current-carrying fault trend prediction method based on least squares support vector machine

A technology of support vector machine and electric equipment, which is applied in the direction of computer components, instruments, characters and pattern recognition, etc., can solve problems such as limitations in the application field, and achieve the effect of simple prediction algorithm, high precision and improved reliability

Inactive Publication Date: 2012-09-12
ZHEJIANG UNIV +1
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Problems solved by technology

But it can only be used in oil-immersed equipment, and the application field is limited

Method used

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  • Power equipment current-carrying fault trend prediction method based on least squares support vector machine
  • Power equipment current-carrying fault trend prediction method based on least squares support vector machine
  • Power equipment current-carrying fault trend prediction method based on least squares support vector machine

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Embodiment Construction

[0026] The invention provides a method for predicting the current-carrying fault trend of electric equipment based on the least squares support vector machine, and the method includes the following steps:

[0027] (1) Select the temperature training sample set, and collect the temperature sequence of the electrical equipment contacts within a certain period of time as the training sample set G={q 1 ,q 2 ,L,q m}, take out d+1 continuous time temperature series values ​​from the training sample set G, the first d as input, and the d+1th as output, thus the training sample set is transformed into: G={(x 1 ,y 1 ), (x 2 ,y 2 ), L, (x i ,y i ), L, (x m-d ,y m-d )}, where x i ={q i ,q i+1 ,L,q i+d-1},y i =q i+d ;

[0028] (2) Establish the least squares support vector machine (LS-SVM) model for the prediction of the development trend of current-carrying faults of power equipment, and use the temperature training sample set as particles, and use the particle swarm optim...

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Abstract

The invention discloses a power equipment current-carrying fault trend prediction method based ona least squares support vector machine. The method provided by the invention comprises the steps of employing historical temperature data to train an LS-SVM regression model, and employing a PSO optimization algorithm to adjust two parameters of the model, namely nucleus width sigma and punishment parameter gamma; employing a PCA algorithm and a K-means clustering algorithm to real-time analyze the temperature of equipment contacts to find contacts with abnormal temperature rising, and using the temperature value asan initial value sequence of prediction;and finally employing the regression model obtained by training to predict the temperature value of the initial value for a long term and for a short term, and analyzing the highest point the contact temperature may reach and the time when the contact temperature reaches the highest point. Through predictive analysis based on PSO-LSSVM, fault development trend of equipment contacts is actively controlled, so the time for timely measures and ensuring the safe operation of power grid is bought. The method provided by the invention can be widely used in the field of power equipment forecast alarm protection.

Description

technical field [0001] The invention relates to a method for predicting the current-carrying fault trend of electric equipment based on the least square support vector machine. Background technique [0002] With the advancement of science and technology and the rapid development of the national economy, the demand and dependence on electricity are getting stronger and stronger, and the requirements for the safety of the power supply system and the good condition of the power equipment are getting higher and higher. There are many kinds of faults in power equipment. Current-carrying faults are caused by poor contact or oxidation at connection points such as cables, switches, and copper bars, resulting in increased contact resistance, resulting in joint melting or even short-circuiting, which may cause cable explosions, large-scale power outages and business failures. Suspension of production and other consequences seriously endanger the normal operation of the power system. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
Inventor 许力张慧源顾宏杰许文才李学红
Owner ZHEJIANG UNIV
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