IFOA-SVM-based high-voltage switch cabinet state evaluation method

Abstract

The invention discloses an IFOA-SVM-based high-voltage switch cabinet state evaluation method. The method comprises the steps: firstly, collecting operation data of a high-voltage switch cabinet, and preprocessing the data through a method of deleting extreme abnormal values and complementing missing values; on the basis of a field investigation result and expert experience, selecting characteristic quantities for evaluating the high-voltage switch cabinet as a partial discharge value, an ultrasonic value, infrared diagnosis, a cable joint temperature and a busbar temperature; then, combining a polynomial function and a Gaussian radial basis function to construct a mixed kernel function, and mapping an original input space to a high-dimensional feature space; then optimizing a kernel function proportionality coefficient, a kernel function width parameter and a penalty factor by using an improved fruit fly optimization algorithm; and finally, by training an OVO-SVM high-voltage switch cabinet state estimation classifier, realizing nonlinear classification of the high-voltage switch cabinet state, namely, high-voltage switch cabinet state evaluation. According to the invention, multi-source information is utilized to carry out state comprehensive evaluation on the distribution high-voltage switch cabinet, a theoretical framework is simple, and the method is easy to implement and high in generalization ability.

Description

technical field [0001] The invention is applied to the field of high-voltage switchgear state assessment, and relates to a high-voltage switchgear state assessment method based on an improved fruit fly optimization algorithm and a support vector machine (IFOA-SVM). Background technique [0002] As one of the main equipment of the power distribution system, the high-voltage switchgear plays the role of controlling and protecting other power equipment in the smart grid. At present, the number of high-voltage switchgear installations is huge and the maintenance tasks are heavy. The failure of high-voltage switchgear has become the main factor affecting the safety and stable economic operation of the distribution network. The health status assessment of high-voltage switchgear has become an urgent research focus. [0003] Because the internal structure of the high-voltage switchgear is very complex, and the components have a great influence on each other, the current state and f...

AI Technical Summary

Problems solved by technology

The traditional state assessment method based on the SVM classification model is mainly to improve the accuracy and efficiency of classification and recognition by finding a better single kernel function, or optimizing the relevant parameters of SVM through modern optimization algorithms, which cannot take into account both the SVM kernel function structure and The improvement of the parameter optimization algorithm has certain limitations

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