Fuzzy fault classification method of electric transmission line

Abstract

A fuzzy fault classification method of an electric transmission line includes the first step of determining the time of occurrence of a fault, the second step of computing fault input vectors, the third step of constructing fuzzy support vector machine FSVM dichotomy devices, the fourth step of training and optimizing the FSVM dichotomy devices, the fifth step of constructing a banding subsection subordinating degree function of a FSVM higher space, the sixth step of enabling the fault input vectors to be input into each FSVM dichotomy device to obtain a preliminary classification label, a decision function value and an initial subordinating degree of each FSVM dichotomy device, the seventh step of constructing and training a support vector regression (SVR), the eighth step of sending the decision function values and initial subordinating degrees into the SVR to obtain a final fault subordinating degree of a fault sample, and the ninth step of judging the final fault type according to the final subordinating degree. According to the fuzzy fault classification method of the electric transmission line, the fuzzy subordinating degree function is introduced, and therefore influences of noise points and isolated points on a SVM hyperplane structure are reduced; the SVR is adopted to perform correction on the preliminary classification labels obtained by the FSVM, the fault classification label is obtained accurately through fuzzification processing, regressive optimization processing and the like, and therefore the accuracy and fault tolerance for fault classification of the electric transmission line are greatly improved.

Description

technical field [0001] The invention relates to the field of troubleshooting and maintenance of power transmission lines in electric power systems, in particular to a fuzzy fault classification method for power transmission lines. Background technique [0002] Transmission line fault classification is extremely important for the normal operation of power systems. At present, there are many features used for fault classification of transmission lines, mainly using Fourier transform to process the original current and voltage signals. Frequency features [3] three types of feature quantities. [0003] Literature [4] uses the Empirical Mode Decomposition (EMD) in the Hilbert-Huang transform to decompose the localization characteristics of the signal, and obtains the singular value entropy of the sample signal, which is used as the feature quantity to classify the fault of the transmission line . [0004] Literature [5] uses EMD to construct the sample entropy of the sample si...

AI Technical Summary

Problems solved by technology

[0011] Although the classification methods in the existing literature use support vector machines to achieve better fault classification results, when the input feature vector is mixed with noise or bad data, the proposed feature quantity has certain fuzziness and complexity. The feature quantity is not linearly separable, and the classification accuracy of existing classification methods will decrease, and even misclassification may occur

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