CPC G01R 31/62 (2020.01) [G01N 33/0004 (2013.01); G06N 3/045 (2023.01); G06N 3/044 (2023.01)] | 16 Claims |
1. A deep parallel fault diagnosis method for dissolved gas in transformer oil, comprising following steps executing by a processor:
step 1: performing an operation of a plurality of transformers of a power system of a power grid and obtaining a plurality of groups of monitoring data of dissolved gas in each transformer oil of the plurality of transformers of the power system of the power grid, analyzing a dissolved gas content in each of the groups of monitoring data, obtaining a corresponding fault type label, performing a normalizing processing on each of the groups of monitoring data, and forming a target data set by combining the normalized groups of monitoring data with the corresponding fault type label;
step 2: dividing the target data set into a first training set and a first verification set, training a long short-term memory (LSTM) diagnosis model with the first training set, and verifying the trained LSTM diagnosis model with the first verification set;
step 3: performing image processing on each group of data in the target data set to obtain an image data set, dividing the image data set into a second training set and a second verification set, training a convolutional neural network (CNN) diagnosis model with the second training set, and verifying the trained CNN diagnosis model with the second verification set;
step 4: performing a deep parallel fusion on outputs of softmax layers of the trained LSTM diagnosis model and the trained CNN diagnosis model, respectively, and outputting a final diagnosis result according to a maximum confidence principle; and
step 5: modifying the operation of the plurality of transformers of the power system of the power grid based on the final diagnosis result.
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