IMABC optimized support vector machine-based transformer fault diagnosis method

Abstract

The invention discloses an IMABC optimized support vector machine-based transformer fault diagnosis method. The method comprises the steps of 1, dividing a collected sample set S={(x1,x2),(x2,y2)...(xn,yn)}, with class tags, of an oil-immersed transformer into training samples and test samples, wherein xi represents sample attributes including five attributes of hydrogen, methane, ethane, ethyleneand acetylene, yi represents the class tags, and 1, 2, 3, 4, 5 and 6 correspond to a normal state, middle temperature overheat, high temperature overheat, local discharge, spark discharge and arc discharge respectively; 2, proposing an improved artificial bee colony algorithm, fusing population classification and gene mutation in the artificial bee colony algorithm, and optimizing parameters of asupport vector machine; and 3, taking Ci and sigma i as the optimized parameters of the support vector machine, building a multilevel support vector machine fault diagnosis model, and performing transformer fault diagnosis by utilizing data in the step 1. According to the transformer fault diagnosis method, the parameters of the support vector machine can be effectively optimized, so that the accuracy of binary classification is improved.

Description

technical field [0001] The invention belongs to the technical field of transformer fault online monitoring, and in particular relates to a transformer fault diagnosis method based on IMABC optimization support vector machine. Background technique [0002] With the rapid development of power grid construction, the pattern of national networking has basically taken shape. The power system is a large system connected by many electrical equipment for transmission, transmission, transmission, and distribution. These equipment directly determine the stability and safety of the entire system. Transformers are widely used in power systems, and their safe and stable operating status is related to the safety of the power grid and people. Failures will bring great inconvenience to people's lives, so it is particularly important to diagnose transformer faults. The main faults of transformers include high temperature overheating, medium and low temperature overheating, partial discharge...

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Problems solved by technology

[0003] At present, a variety of monitoring methods have been proposed for the above faults, such as: dissolved gas analysis technology in oil, iron core grounding current detection technology and infrared thermal image detection technology; among them, dissolved gas analysis technology in oil contains rich operating status information, which can be used as The basis for transformer fault diagnosis, but because it relies too much on manual experience and cannot directly pass the threshold judgment, it needs to use artificial intelligence algorithms, such as fuzzy theory, BP neural network, etc. for fault diagnosis

However, fuzzy control has certain human factors in the determination process of membership function and fuzzy rules, and the fault diagnosis based on BP neural network algorithm has the disadvantages of slow convergence speed and easy to fall into local minimum.

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