Transformer partial-discharging mode recognition method based on singular value decomposition algorithm

Abstract

The invention discloses a transformer partial-discharging mode recognition method based on a singular value decomposition algorithm, and the transformer partial-discharging mode recognition method comprises training model and classification recognition process, and the method comprises the steps of firstly establishing an artificial defect experimental environment, collecting data samples, calculating statistic characteristic parameter of each sample to form a data sample matrix; conducting singular value decomposition for the sample matrix, determining an order of an optimum reserved matrix by judging whether the characteristic of the reserved matrix is obvious or not, and obtaining a type characteristic space description matrix after the dimensionality reduction and a class center description vector group; preprocessing the sample to be recognized to obtain a sample vector, utilizing the type characteristic space description matrix to linearly convert the sample vector to obtain the sample description space vector after the dimensionality reduction, and then calculating the similarity of the vector with each vector in the type vector group to obtain a classification judgment result. The algorithm is simple and high efficient, reliability for distinguishing an interference signal and a discharging signal in the partial-discharging detection can be realized, and the accuracy for diagnosing the partial-discharging mode can be improved.

Description

Technical field [0001] The invention belongs to the field of electric power technology, and more specifically, relates to a method for identifying partial discharge patterns of transformers based on a singular value decomposition algorithm. Background technique [0002] Partial discharge is one of the main reasons leading to the deterioration of the internal insulation of large power transformers. On-line monitoring of the partial discharge of the transformer can timely and accurately determine the internal insulation status of the transformer, which is of great significance to prevent the occurrence of power transformer accidents. The two main problems of partial discharge pattern recognition methods are the selection of feature quantities and the design of classifiers. When selecting statistical characteristic parameters as partial discharge characteristic quantities, the existing technology can directly select several statistical parameters as characteristic quantities. This m...

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Problems solved by technology

When selecting statistical characteristic parameters as PD characteristic quantities, the prior art either directly selects several as characteristic quantities from numerous statistical parameters, and this method lacks scientific basis entirely based on practical experience; or adopts the characteristic based on principal component analysis algorithm Selection method, but the process of this method is complicated, and the algorithm implementation is difficult

[0003] In terms of classifier construction, the existing technology mainly adopts the classification method based on BP (Back Propagation) neural network algorithm. This method is: sensitive to the selection of initial weights and thresholds; easy to fall into local minimum points, resulting in learning process Failure; algorithm convergence speed is slow, low efficiency and other deficiencies

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