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...

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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 ...

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