Fault classification method based on improved fuzzy support vector machine

A fuzzy support vector, fault classification technology, applied in fault location, measuring electricity, measuring devices, etc., can solve problems such as poor effect, ignoring interaction and dispersion, etc.

CN106203500AActive Publication Date: 2016-12-07HANGZHOU ELECTRIC EQUIP MFG +1
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2016-12-07

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Abstract

The invention discloses a fault classification method based on an improved fuzzy support vector machine. The method comprises the steps that sample data of a switch cabinet are acquired, an optimization model is established for the sample data by introducing a relaxation factor, and the minimum radius of the hyper-sphere of the optimization module is calculated; the membership degree of the sample data inside and outside the boundary of the hyper-sphere is calculated by using a segmented half-time normal cloud model with the minimum radius of the hyper-sphere acting as the boundary of the hyper-sphere; the sample data of the switch cabinet, fault classification marks and the membership degree form a sample set of the fault types, and the optimal segmented function of the improved FSVM is acquired by using the sample set of the fault types; and the classifier of the improved FSVM is established by using the optimal segmented function of the improved FSVM, and the sample data pass through the classifier of the improved FSVM in turn so that the fault type of each sample data is acquired. The effect of fault classification of the switch cabinet can be enhanced by the method.
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Description

technical field

[0001] The invention relates to the technical field of fault classification, in particular to a fault classification method based on an improved fuzzy support vector machine. Background technique

[0002] At present, the operation status of cables, busbars and circuit breakers is monitored, fault characteristic parameters are extracted, and typical fault classification and identification can be realized by using fault characteristic data, which is conducive to the identification and diagnosis of various fault states and guides its operation and maintenance management. For the classification and identification of faults, there are currently decision tree classification, Bayesian classification, neural network classification and support vector machine classification, among which support vector machine (Support Vector Machine, SVM) can obtain training learning accuracy and test accuracy on small sample data. The optimal compromise between recognition capabilitie...

Examples

Embodiment Construction

[0041] The core of the invention is to provide a fault classification method based on the improved fuzzy support vector machine, so as to realize the effect of improving the fault classification of switch cabinets.

[0042] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0043] Please refer to figure 1 , figure 1 A flow chart of a fault classification method based on an improved fuzzy support v...