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Improved Adaboost-SVM model generation method suitable for wind power converter fault diagnosis

A wind power converter and fault diagnosis technology, applied in the measurement of electrical variables, instruments, measurement of electricity and other directions, can solve the problems of large influence, performance degradation, poor diagnosis accuracy, etc., to solve the performance degradation and improve the generalization ability.

Pending Publication Date: 2019-06-07
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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Problems solved by technology

However, there are still many problems in the research process. The traditional Adaboost integrated SVM fault diagnosis model is greatly affected by fault samples, and performance degradation will occur, resulting in poor diagnostic accuracy, which has certain limitations (Reference 10 )

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  • Improved Adaboost-SVM model generation method suitable for wind power converter fault diagnosis
  • Improved Adaboost-SVM model generation method suitable for wind power converter fault diagnosis
  • Improved Adaboost-SVM model generation method suitable for wind power converter fault diagnosis

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Embodiment Construction

[0043] Adaboost is an iterative algorithm. Its essence is to use the same training sample set to train the base classifier, and then combine multiple trained base classifiers to make decisions together to form a strong classifier. The iterative process of Adaboost algorithm is the process of strengthening the weak classification algorithm. The weight of each training sample is given, and the weight of each sample is updated according to the classification error rate of the previous round. In the process of algorithm training, the weight of misclassified samples is increased, and the weight of correctly classified samples is decreased. Through multiple iterations, a strong classifier is finally obtained by combining multiple weighted weak classifiers.

[0044]By analyzing and researching the derivation of the Adaboost algorithm, it is found that some samples are easily misclassified many times during the iterative process of the algorithm, which leads to an upward trend in the...

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Abstract

The invention relates to an improved Adaboost-SVM model generation method suitable for fault diagnosis of a wind power converter, and the method comprises the steps: giving the weight of each trainingsample, and updating the weight of each sample according to the classification error rate of the previous round. In the algorithm training process, the weight of the mistakenly classified samples isincreased, and the weight of the correctly classified samples is reduced. Through multiple iterations, a plurality of weighted weak classifiers are combined to finally obtain a strong classifier. Through the design of the improved Adaboost-SVM model, the problem of performance degradation caused by different samples can be solved to a certain extent, the generalization capability of the classifieris improved, and the method has a very good effect in application to fault diagnosis of the converter.

Description

technical field [0001] The invention relates to a power fault judgment technology, in particular to an improved Adaboost-SVM model generation method suitable for wind power converter fault diagnosis. Background technique [0002] Wind power plays an extremely important role in the field of electrical energy. As the hub between the power generation system and the grid (Document 1), the converter can not only ensure the stability of electric energy under random wind speed but also meet the requirements of the grid. Converters are usually in harsh working environments for a long time, and are prone to failures (References 2 and 3). The short circuit and open circuit of the power switching device (Insulated Gate Bipolar Transistor, IGBT) of the converter are the two most common types of faults of the converter. When the IGBT is short-circuited, a large current will flow within a very short time limit, burning out the protection device connected in series with it, and finally s...

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Application Information

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IPC IPC(8): G06K9/62G01R31/00
Inventor 郑小霞彭鹏
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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