Method for predicting concentration of gas dissolved in transformer oil

Abstract

The invention discloses a method for predicting concentration of gas dissolved in transformer oil. The method includes: step 1, conducting necessary processing on data of the gas dissolved in the transformer oil, forming equal interval data, A=[a1, a2,...,an], and building a function on training sample data input X and output Y according to a certain method; step 2, building a fast relevance vector machine model; step 3, building dereferencing of fitness function optimizing nuclear parameters and optimizing a kernel function by using a particle swarm optimization algorithm and a leave-one-outmethod; and step 4, leading training date into the fast relevance vector machine model to obtain relevance vector and corresponding weight. The method utilizes a relevance vector machine fast algorithm to overcome defects of a traditional relevance vector machine of being slow in calculating. By using the particle swarm optimization algorithm and optimizing the kernel function on the basis of theleave-one-out method, reliability of data and complexity of calculation are well balanced, and an accurate predicting result is obtained.

Description

technical field [0001] The invention relates to a method for predicting the concentration of dissolved gas in oil, in particular to a method for predicting the concentration of dissolved gas in transformer oil. Background technique [0002] In recent years, many different methods have been used to predict the concentration of dissolved gases in transformer oil, including gray model and its improved model, artificial neural network and support vector machine (Support Vector Machine, SVM), etc. Artificial neural network is widely used in data prediction, but it requires a large amount of data, and relatively long-term data will affect the accuracy of gas concentration prediction. The prediction of dissolved gas concentration in oil presents the characteristics of small samples, so artificial neural network is not applicable For the prediction of dissolved gas concentration in transformer oil; the gray model can get higher prediction accuracy for small sample problems, but it d...

AI Technical Summary

Problems solved by technology

Artificial neural network is widely used in data prediction, but it requires a large amount of data, and relatively long-term data will affect the accuracy of gas concentration prediction. The prediction of dissolved gas concentration in oil presents the characteristics of small samples, so artificial neural network is not applicable For the prediction of dissolved gas concentration in transformer oil; the gray model can get higher prediction accuracy for small sample problems, but it describes a process that increases or decreases exponentially with time, and the concentration of dissolved gas in oil is affected by The influence of the external environment sometimes does not conform to this rule, so the gray model always has a certain deviation in the prediction; the support vector machine has been widely used in the prediction problem due to its excellent performance in dealing with small sample problems , which also shows good performance in the prediction of dissolved gas in oil, but because it needs to set too many parameters, the complexity of the algorithm increases

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