Photoacoustic spectroscopy-based transformer fault diagnosis method employing parameter optimization SVM (support vector machine)

Abstract

The invention discloses a photoacoustic spectroscopy-based transformer fault diagnosis method employing a parameter optimization SVM (support vector machine). According to the photoacoustic spectroscopy, contents of five characteristic gases of transformer oil are detected and calculated; 5 SVMs and 4 kernel functions are subjected to cross combination, and 20 SVM models are formed; the heuristic algorithm is adopted to perform parameter optimization on values of penalty factors c and g, and accordingly the SVM model highest in transformer fault diagnosis accuracy and operation speed is established. The experimental result shows that the SVM model composed of a C-SVC (C-support vector classification) model, an RBF (radial basis function) and the genetic algorithm optimization is highest in transformer fault diagnosis accuracy, with test set of 97.5% and training set of 98.3333%, and the optimizing speed of the genetic algorithm is faster than the particle swarm optimization by double. The photoacoustic spectroscopy-based transformer fault diagnosis method has the advantages such as simple operation, non-contact measurement, no consumption of carrier gas, short detection period, stability and high sensitivity.

Description

technical field [0001] The invention belongs to the field of transformer fault diagnosis, and in particular relates to a transformer fault diagnosis method based on photoacoustic spectroscopy using a parameter optimization support vector machine. Background technique [0002] The reliable operation of power transformers is the key to ensuring the safety of power systems. The improved three-ratio method recommended by the Electric Power Industry Standard of the People's Republic of China "Guidelines for the Analysis and Judgment of Dissolved Gases in Transformer Oil DL / T 722-2000" is the current domestic and foreign analysis of transformer latent One of the most effective measures for permanent faults is to determine the transformer fault type by measuring the characteristic gas content in the transformer oil and according to the characteristic gas ratio C2H2 / C2H4, CH4 / H2, C2H4 / C2H6. Characteristic gas detection mainly uses gas chromatography, but it has disadvantages such as...

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Problems solved by technology

[0005] The purpose of the embodiments of the present invention is to provide a transformer fault diagnosis method based on photoacoustic spectroscopy using parameter optimization support vector machine, aiming to solve the problems of cumbersome operation and consumption of gas to be tested in the fault diagnosis of transformer gas chromatography analysis And carrier gas, long detection cycle and other issues

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