96 results about "Transform fault" patented technology

The invention discloses a transformer fault diagnosis method based on improved principal component analysis, which belongs to the technical field of transformer fault diagnosis. The method adopts the sum of absolute values of sample indexes to carry out standardized treatment on sample index values, and thus, the difference of each sample index value in magnitude can be eliminated, and the information difference features between samples can also be kept; and sample principal components are selected according to cumulative contribution rates of the principal components, Euclidean distances among the sample principal components are clustered, and a transformer fault type is judged. The method of the invention can effectively improve the accuracy for diagnosing inner hidden fault of the transformer.

The invention discloses a transformer fault diagnosis method based on Bi-LSTM and analysis of dissolved gas in oil. The method comprises: collecting fault DGA monitoring data of each substation, carrying out normalization, sequence expansion, noise superimposing and the like on the data, and extracting fault feature information based on a non-coding ratio method; carrying out length ranking on a DGA sequence, carrying out grouping and filling, and classifying groups into a training set and a verification set; constructing a deep learning frame based on Bi-LSTM, inputting data, and carrying outtraining; and then carrying out diagnosis and network updating by combining actual test data to obtain a fault diagnosis model with the high diagnosis accuracy and portability. According to the invention, the influence of the noise and error on the diagnosis during the DGA data monitoring process is reduced effectively; and the Bi-LSTM-based transformer fault diagnosis model is constructed by considering the complex correlation between different sequences. With introduction of links of sequence sorting, grouping, filling and the like, a problem of different sampling lengths of different transformers in the actual engineering is solved by using the batch training strategy.

The invention discloses a vibration data fusion and image recognition-based transformer fault diagnosis method. A plurality of vibration measuring points are arranged on the outer surface of a transformer oil tank to measure vibration data so as to obtain vibration data under various typical fault states of the transformer. Characteristic value extraction is performed on the pieces of vibration data respectively to form characteristic value matrixes which completely describe the process change rule of the various typical faults, i.e., a sample matrix database of the various typical faults. On the basis, the image recognition technology is applied to the field of transformer fault diagnosis; through matching calculation of the characteristic value matrixes, the defects of the electric parameter measuring method and the artificial fault recognition in the field of the conventional transformer fault diagnosis are overcome, the faults and the states of transformer windings and cores can be sensitively reflected, and the potential faults in the transformer are earlier discovered.

The invention discloses a transformer fault diagnosis method based on a conditional generative adversarial network. The method includes the following steps: (1) collecting transformer state historicalmonitoring data, forming a transformer fault diagnosis data set, and dividing the transformer fault diagnosis data set into a training set and a test set; (2) constructing a conditional generative adversarial network into a multi-layer sensor structure, wherein the conditional generative adversarial network has a generator and a discriminator and N outputs of the discriminator comprise: N-1 faulttypes and a judgment result about whether the data comes from analog fault data of the generator; (3) training and testing the generator and discriminator; and (4) inputting collected real-time monitoring data of the state of a transformer into the discriminator, and obtaining a fault diagnosis result of the transformer from the output of the discriminator. In addition, the invention discloses asystem based on a conditional generative adversarial network. The transformer fault diagnosis method can realize automatic identification of defects of transformer equipment.